carbon dynamics of a new england temperate forest five
TRANSCRIPT
Carbon Dynamics of a New England Temperate Forest Five Years After Selective Logging
Frances C. O’Donnell
Senior Thesis in Organismic and Evolutionary Biology
Advisor: Professor Steven Wofsy
April 2007
Acknowledgements
I would like to thank my advisor, Professor Steven Wofsy, for giving me the opportunity to participate in research and write a senior thesis as a member of his research group. The experience has been invaluable in shaping my academic and career goals and preparing me for the future. I am indebted to Wofsy group members Kathryn McKain and Daniel Curran for their wonderful help and mentorship through every stage of the research and writing process. Thanks are also due to Elizabeth Hammond Pyle, Lucy Hutyra, and Dr. William Munger for their helpful feedback on my data analysis and writing, and to John Budney and Research Experience for Undergraduates (REU) student Alison Grantham for help with field work this past summer.
I would like to thank my parents for their help with proofreading and for their continued love and support. I am also thankful for the encouragement and camaraderie of my friends through the ups and downs of writing a thesis.
Field work for 1999-2005 was performed by Wofsy group members including Christine Jones, David Bryant, and V.Y. Chow, and REU students Daniel Gonzalez-Kreisberg (2004), Zachary Liscow (2003), Sarah Pears and Greg Santoni (2002), Brigid Curry and Julia Silvis (2001), Shane Heath and Felicia Frizzell (2000), and David Patterson (1999).
This research was funded by NSF LTER & REU programs (0080592 & 0139495), the Office of Science, BER, US DOE through the Northeast Regional Center of the National Institute for Global Environmental Change under cooperative agreement no. DE-FCO2-03ER63613, the Terrestrial Carbon Program DE-FG02-03ER83751, and the Harvard College Research Program.
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Table of Contents Abstract......................................................................................................... 5 1. Introduction.............................................................................................. 6 1.1 Greenhouse gases and the global carbon cycle.................................. 6 1.2 Carbon accumulation in forests ......................................................... 9 1.3 Forest harvesting and carbon sequestration ....................................... 11 1.4 Carbon sequestration in wood products............................................. 12 1.5 Forest harvesting in New England..................................................... 14 1.6 Study objectives ................................................................................. 15 2. Methods..................................................................................................... 17 2.1 Study site............................................................................................ 17 2.2 Carbon budget.................................................................................... 20 2.3 Live biomass measurements .............................................................. 21 2.4 Woody debris survey ......................................................................... 22 2.5 Coarse woody debris respiration model............................................. 23 2.6 Belowground biomass and soil carbon fluxes ................................... 32 2.7 Wood products accounting ................................................................ 32 2.8 Ecological measurements................................................................... 34 3. Results ....................................................................................................... 36 3.1 Live biomass ...................................................................................... 36 3.2 Woody debris ..................................................................................... 41 3.3 Coarse woody debris respiration........................................................ 44 3.4 Wood products ................................................................................... 46 3.5 Carbon budget.................................................................................... 50 3.6 Sapling abundance ............................................................................. 56 3.7 Leaf area index................................................................................... 57 4. Discussion.................................................................................................. 59 4.1 Changes in stand dynamics................................................................ 59 4.2 Coarse woody debris pool dynamics ................................................. 60 4.3 Uncertainty in the CWD respiration model ....................................... 61 4.4 Uncertainty in soil carbon and belowground biomass fluxes ............ 64 4.5 Carbon sequestration in wood products............................................. 65 4.6 Source-sink dynamics of the harvest site........................................... 66 4.7 Management implications.................................................................. 67 5. Conclusions............................................................................................... 70 6. References................................................................................................. 71 Appendix A: Allometric Equations ............................................................ 80
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Appendix B: Woody Debris Survey and Calculation ............................... 82 B.1 CWD type classifications .................................................................. 82 B.2 WD volume equations....................................................................... 82 B.3 WD decay class criteria..................................................................... 82 B.4 WD density values ............................................................................ 83 Appendix C: Wood Products Accounting.................................................. 84 C.1 Conversion factors............................................................................. 84 C.2 HARVCARB retirement equations................................................... 85 C.3 LandGEM input statistics.................................................................. 85
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Abstract Rising atmospheric carbon dioxide levels are mitigated by carbon sequestration in the terrestrial biosphere, including temperate forests in the northern hemisphere. Many of the second-growth mixed hardwood forests that cover large parts of eastern North America are significant carbon sinks and are under management that includes harvesting for economic gain. This study examines the effect of a timber harvest typical of the North Quabbin region in north central Massachusetts on the carbon source-sink dynamics of the stand, both annually and cumulatively since the time of harvest. We used plot-based biometric measurements to estimate annual fluxes in the live and dead aboveground carbon pools from 2000 through 2006 in a tract of forest that was selectively logged in 2001. As a control, these measurements were compared to analogous biometric measurements done on an adjacent tract of forest in the footprint of an eddy flux covariance measurement site. We estimated coarse woody debris (CWD) respiration with a linear regression model to compare the dynamics of this pool on the two sites, and tracked the fate of harvested wood by species and timber grade to estimate the annual return of carbon to the atmosphere from removed sawtimber and firewood. Net annual carbon sequestration in on-site carbon pools was initially suppressed by the harvest but recovered to uptake rates similar to those observed on the control site by 2004. Cumulatively since the harvest, the site was a small net carbon sink. If carbon storage in wood products and fossil fuel offsets from firewood use are considered, the system is a larger sink but stored about half as much carbon as the control site over the same time interval. The future carbon dynamics of the harvest site will depend on the trajectories of uptake in tree growth and woody debris respiration as the stand matures.
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1. Introduction 1.1 Greenhouse gases and the global carbon cycle
Carbon dioxide is one of several trace atmospheric gases that, through the
greenhouse effect, help to make the earth a habitable environment by maintaining a
comfortable range of surface temperatures. Solar energy enters the climate system as
short wave radiation, mostly visible light, and a portion of this energy warms the land and
oceans. These surfaces release the absorbed energy to the atmosphere as infrared
radiation and sensible and latent heat. Greenhouse gas molecules absorb infrared
radiation moving away from the earth’s surface and emit it in all directions returning a
portion of the radiated heat back to the earth. This is one component of the global energy
budget (Figure 1.1). In the absence of anthropogenic influences, incoming and outgoing
radiation are equal in this budget, which ensures climatic stability (Kiehl and Trenberth,
1997).
Figure 1.1 Mean annual global energy budget (Wm-2) in the absence of anthropogenic influence. The balance between incoming and outgoing radiation in the global energy budget ensures a stable climate. Back radiation of energy released from the earth’s surface by greenhouse gases is an essential part of the climate system, but can result in an imbalance between incoming and outgoing radiation if the concentration of greenhouse gases is altered by anthropogenic activity. Figure reproduced from Kiehl and Trenberth (1997).
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Human activities have affected the global energy balance through the increase in
greenhouse gas concentrations that has accompanied the rise of agriculture and industry.
Scientists now agree with very high confidence (> 90% chance) that these anthropogenic
influences are having a net warming effect. Evidence for this includes the fact that 11 of
the 12 years with the warmest global surface temperatures in the instrumental record
(since 1850) occurred between 1995 and 2006. Over the last 50 years, there has been a
warming trend of 0.13 ± 0.03°C per decade. The oceans have absorbed over 80% of the
excess heat in the climate system and sea level rise at a rate of 0.31 ± 0.07 m per century
was observed between 1993 and 2003 due primarily to melting of the Greenland and
Antarctic ice sheets and the thermal expansion of water (IPCC, 2007).
The net perturbation to the global energy balance attributed to emission of a
greenhouse gas can be quantified as the anthropogenic radiative forcing for that gas. Five
greenhouse gases, CO2, CH4, N2O, CFC-11 and CFC-12, account for 97% of the total
direct anthropogenic radiative forcing by long-lived gases (Hofmann et al., 2006).
Though the global warming potential (based on per mass radiative forcing and
atmospheric lifetime) for CO2 is much lower than for the other major greenhouse gases
(Sihra et al., 2001), the high rate of CO2 emission makes it the largest and most rapidly
increasing contributor to anthropogenic radiative forcing from long-lived gases, currently
accounting for 62% of the total (Hofmann et al., 2006).
Since the industrial revolution, atmospheric CO2 concentrations have increased
from an average of 278 ppm in 1750 to 379 ppm in 2005, largely due to the burning of
fossil fuels and land-use change (IPCC, 2007). Though the rate of increase in
atmospheric CO2 is very likely (> 90% chance) to be unprecedented in the last 10,000
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years (IPCC, 2007), it is much lower than the rate expected based on emissions. Fossil
fuel burning released 6.3 ± 0.4 PgC/yr between 1990 and 1999, and land use change
accounted for even more carbon flux to the atmosphere. However, the atmospheric CO2
pool only increased by 3.2 ± 0.1 PgC/yr during the same period (IPCC, 2001). Of the
“missing” carbon that did not stay in the atmosphere, about half was dissolved in the
oceans (Sabine et al., 2004), and half was taken up by the land biosphere. Atmospheric
inversion models show that this terrestrial carbon sequestration is spatially variable
(Gurney et al., 2002).
North American temperate forests are considered to be a significant carbon sink
based on multiple lines of evidence including atmospheric models (Fan et al., 1998 and
Pacala et al., 2001) and empirical studies using both eddy-flux covariance data and
biometric measurement of forest carbon pools (Curtis et al., 2002). Several mechanisms
for this net uptake have been suggested. Elevated atmospheric nitrogen deposition was
cited as a possible reason for terrestrial carbon sequestration (Holland et al., 1997).
However, a more recent modeling study found that areas affected by nitrogen deposition
are also areas of high tropospheric ozone concentrations, and the deleterious effects of
this pollutant on plant health nearly offset the expected growth increase (Ollinger et al.,
2002).
Plant fertilization by increased CO2 levels and forest regrowth following
agricultural abandonment have also been suggested as drivers of increased uptake
(Schimel et al., 2000), though there has been continuing disagreement about which of
these is the dominant factor. Some models suggest that CO2 fertilization is more
important (Houghton et al., 1999; McGuire et al., 2001; Jain and Yang, 2005) while other
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models (Schimel et al., 2001; Hurtt et al., 2002) and forest inventory analyses (Caspersen
et al., 2000; Pacala et al., 2004) concluded that forest regrowth was dominant. Most
recently, Albani et al. (2006) used the ecosystem demography model (Moorcroft et al.,
2001) to simulate carbon uptake rates in the eastern U.S. in the 1980s and 90s with and
without CO2 fertilization. They compared the results of these simulations with observed
uptake rates from forest inventory analysis and eddy-flux covariance data and concluded
that land-use history factors are most important and that forest harvesting would be a
dominating influence on the future carbon dynamics of these forests.
1.2 Carbon accumulation in forests
The rate of carbon influx into or efflux from an ecosystem is defined as net
ecosystem productivity (NEP) and is estimated as the difference between total
photosynthetic gain or gross primary productivity (GPP) and ecosystem respiration (R)
(Woodwell and Whittaker, 1968). A long-standing ecological hypothesis (Odum, 1969)
states that as an ecosystem develops following a major disturbance, NEP peaks and
declines to zero as the system reaches maturity (Figure 1.2). There is some empirical
evidence for this hypothesis in forest ecosystems from studies of aboveground net
primary productivity (NPP), the net addition to aboveground biomass of live plants, in
forest age chronosequences. A meta-analysis of these studies found that aboveground
NPP differed by as much as 76% between developing and mature stands with an average
difference of 34% (Gower et al., 1996). The decreased aboveground NPP in older stands
may be due in part to changes in allocation, with belowground growth increasing as the
soil nutrient supply declines in a maturing stand (Ryan et al., 2004).
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Figure 1.2 The energetics of forest succession. Expected pattern of gross primary productivity (PG), respiration (R), net ecosystem productivity (shaded area, PN), and biomass accumulation (B) during forest development following a large disturbance. Figure reproduced from Odum (1969).
A long-term study of carbon exchange in a 75-110 year old mixed hardwood
forest at the Harvard Forest Environmental Measurement Site (HFEMS) in north central
Massachusetts suggests that NEP is positive and increasing in the temperate forests of
New England (Wofsy et al., 1993; Barford et al., 2001; Urbanski et al., in press). Since
1990, the eddy-flux covariance method (Baldocchi et al., 1988) has been used at this site
to measure the net ecosystem exchange (NEE) of CO2 between the forest and the
atmosphere. Simultaneous ground-based measurements of forest carbon pools have also
taken place at the HFEMS. These measurements are used to relate the overall NEE
observed with eddy-flux covariance to fluxes in forest carbon pools such as live biomass
and woody debris (Barford et al., 2001). These two methods of measurement indicated
that carbon uptake at the HFEMS averaged 2.5 MgC ha-1 yr-1 between 1992 and 2004,
and uptake increased in magnitude by 0.15 MgC ha-1 yr-1 (Urbanski et al., in press).
Primary productivity at this site is dominated by red oak, and a study of the growth rate
of this species in stands of varied age across New England, including the HFEMS as one
of the mid-successional stands in the study, found that red oak productivity tended to
increase with stand age (Pederson, 2005). This suggests that the carbon dynamics of
these red oak forests, where uptake rates are increasing over time, are similar to those
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predicted by Odum’s model for an early-successional forest. Eddy-flux measurements in
a 200-year-old hemlock stand at the Harvard Forest in 2001 found that it was a carbon
sink of slightly lower magnitude than the mixed hardwood stand in that year (Hadley and
Schedlbauer, 2002), so it is uncertain at what age New England forests will reach a point
where NEP is close to zero.
1.3 Forest harvesting and carbon sequestration
It has been suggested that occasional harvesting may increase the potential of a
forest to store carbon by returning mature stands to an earlier successional stage when
hypothetical NEP is higher (Roxburgh et al., 2006; Laclau, 2003). In cases where GPP
declines with forest age, nutrient limitation in a closed-canopy stand and decreased
hydraulic conductance with increased tree height appear to be the major causes of the
decline in GPP, while decreased growth due to the aging of trees is not considered to be a
factor. This makes it possible to increase the GPP of a stand by thinning it (Martinez-
Vilalta et al., 2007).
While it is possible that GPP is higher in recently harvested forests, the resulting
carbon uptake may be offset by fluxes to the atmosphere that result from the disturbances
associated with harvesting, so leaving mature forests undisturbed may be the best strategy
for maximizing carbon storage. Modeling studies estimated that logging of old growth
forests would result in large fluxes of CO2 to the atmosphere (Harmon et al., 1990; Song
and Woodcock, 2003). An analysis of the carbon storage potential of Alaska’s Tongass
National Forest under different future management regimes predicted that cessation of
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logging would maximize carbon sequestration over the next century (Leighty et al.,
2006).
Existing empirical data on harvesting and carbon sequestration are limited to
chronosequence studies in boreal (Howard et al., 2004; Martin et al., 2005) and tropical
forests (Lasco et al., 2006). Among these studies, there is disagreement on the effect of
harvesting. In a study of boreal mixedwood and jack pine logging chronosequences in
Manitoba, NPP differed by 24% between the oldest and youngest stand and was greatest
in a stand harvested 11 years before the study (Martin et al., 2005). However, on a
group of similar sites in Saskatchewan, the most recently harvested site was a significant
carbon source while the other sites were slight sinks or sources (Howard et al., 2004). A
carbon accounting study of a logging chronosequence and sawmill in an area of South
Asian forest that is logged on a 35-year cutting rotation found that stands only reached
70% of pre-harvest carbon stocks before they were harvested again and in each harvest
about 60% of the carbon in aboveground biomass was released to the atmosphere. The
authors concluded that the forest is not an efficient and sustainable carbon sink because
the current harvesting rotation is too short (Lasco et al., 2006). More conclusive data on
harvesting and carbon dynamics are needed before forest management plans can be made
with the goal of preserving or increasing carbon sequestration in temperate forests,
particularly data that tracks the source-sink dynamics of a single site following a harvest.
1.4 Carbon sequestration in wood products
In addition to measuring the on-site carbon fluxes following a harvest, the fate of
carbon in the removed wood must be determined to understand the overall biosphere-
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atmosphere carbon flux resulting from a harvest. Some carbon is stored in long-lived
products such as furniture and construction material and returned to the atmosphere
slowly through decay in landfills, while carbon in fuelwood or short-lived paper products
is returned to the atmosphere more quickly (Skog and Nicholson, 1998; Marland and
Marland, 2003). Though a significant portion of the carbon in harvested wood is returned
to the atmosphere, the use of some wood products may offset fossil fuel emissions.
Wood burned for energy, as firewood in homes or as sawmill residues, which may be
burned at the sawmill or sold as fuel, offsets fossil fuel burning, though there are many
energy requirements in the harvesting and processing of wood that must also be
considered (Gan and Smith, 2006; Raymer, 2006). Using wood in construction may
reduce carbon fluxes to the atmosphere by replacing cement, steel, brick and other
materials that require greater fossil fuel consumption in production (Werner et al., 2005;
Gustavsson and Sathre, 2006).
Forest carbon pools and wood product carbon pools were considered together in a
modeling study that found that the source-sink dynamics of the system as a whole were
highly dependent on the length of the harvesting rotation, with longer rotations promoting
carbon sequestration (Perez-Garcia, 2005). The results were also highly dependent on the
amount of detail used in accounting for things like the fuel substitution of wood burned
for energy and the decay rate of wood in long-term storage pools. Empirical data
considering both forest pools and wood products are needed for comparison with model
results.
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1.5 Forest harvesting in New England
The second-growth, temperate forests of New England are an ideal setting for a
study of the effects of harvesting on forest carbon dynamics, because carbon exchange in
forests not subjected to recent harvesting has been studied extensively at the Harvard
Forest and because logging is fairly common. The Harvard Forest is part of the heavily
forested North Quabbin region, a 168,000 ha landscape that includes the Quabbin
reservoir, which supplies water to metropolitan Boston. Between 1984 and 2000, 26% of
the region’s forests were harvested at least once with the average harvest removing about
one fourth of the stand volume. Each year approximately 1.5% (2,000 ha) of the forested
area is affected by new harvesting disturbance, with most harvesting occurring in small
operations on parcels of land controlled by over 2,500 non-industrial private forest
owners in the region (Kittredge et al., 2003). Comparison of harvesting activity in the
North Quabbin with databases for Massachusetts (Dickson and McAfee, 1988) and the
Northeastern U.S. (Birch, 1996) shows that the region is broadly representative of forest
management across Massachusetts and the Northeast (Kittredge et al., 2003).
In an effort to synthesize the management plans of the many government, non-
profit, and private landowners in the North Quabbin and across Massachusetts, Foster et
al. (2005) published Wildlands and Woodlands, a large-scale management proposal for
the forests of Massachusetts. In this report, the authors identified the goals for forest
management in the Northeast: a sustainable source of wood products, maintenance of an
assemblage of habitats that promotes biodiversity, areas for recreational and educational
experiences, and preservation of ecosystem processes that provide clean air and water,
productive soils, and natural flood control. The report does not specifically identify
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carbon sequestration as a goal for management, perhaps due to the lack of data on the
effect that management decisions may have on this aspect of ecosystem functioning.
Incentives for land owners to make management decisions that increase carbon
sequestration could be incorporated into cap-and-trade programs for carbon emissions,
which are emerging as a strategy for dealing with greenhouse gases in New England and
other parts of the country. Under this type of program, a maximum level of emissions is
set for a region and emissions allowances are traded on the free market, making
emissions-reducing measures a potentially lower-cost option. Cap-and-trade programs
are a good option for controlling greenhouse gases because these pollutants have the
same effect regardless of where they are released and the cost of reducing emissions is
highly variable among different sources (Ellerman et al., 2003). Massachusetts recently
joined the Regional Greenhouse Gas Initiative (RGGI), a cap-and-trade program for CO2
emissions by power plants in nine Northeast and Mid-Atlantic states. Under the current
RGGI memorandum of understanding, a source may cover up to 3.3% of its carbon
emissions with carbon offsets such as landfill gas capture and combustion and
afforestation of cleared land, and the signatory states have agreed to develop other offset
categories including certain forestry practices (RGGI, 2005). Before forest management
could be included in a program like this, more data is needed on the consequences of
harvesting for carbon sequestration.
1.6 Study objectives
This study examines the carbon dynamics of a second-growth hardwood forest in
north-central Massachusetts that was selectively logged in 2001 using biometric
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measurement of forest carbon pools and life-cycle analysis of removed saw timber and
firewood. Both the on-site and overall source-sink dynamics are analyzed on an annual
basis and cumulatively since the harvest by constructing a carbon budget that includes
measurements and estimates of forest carbon pools, carbon storage and fluxes from wood
products, fossil fuel emission offsets from the burning of wood for energy, and energy
use in harvesting, transport and processing of wood products. The consequences of the
results for forest management in New England and the consideration of global-scale
consequences in local and regional management plans are addressed.
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2. Methods
2.1 Study site
The harvest and control sites are located in the town of Petersham in north-central
Massachusetts (42.5 N, 72.2 W). The area is nearly continuously covered by mixed
hardwood forest. Dominant species are northern red oak (Quercus rubra) and red maple
(Acer rubrum) with some eastern hemlock (Tsuga canadensis), American beech (Fagus
grandifolia), eastern white pine (Pinus strobus), and several species of birch (Betula
spp.). Most forests in the region established after intense, land-use related disturbances
ceased. In the mid-1800’s, 70% of the land was cleared for pasture and 17% was tilled.
The remaining forests were used as woodlots. Throughout the late 19th and early 20th
centuries, farms were gradually abandoned and reforested. Large areas of this second-
growth forest were logged and some clear cut areas were converted to conifer plantations
(Foster, 1992). Many areas were disturbed by a major hurricane followed by a salvage
harvesting operation in 1938 (Foster et al., 1997).
The control site is part of the Prospect Hill research tract of the Harvard Forest.
An analysis of tree ring data and historical records found that most of the canopy trees on
the site established following large timber harvests between 1890 and 1895 and forest
management that occurred from 1925 to 1934. The stand only suffered slight damage in
the 1938 hurricane and this disturbance does not appear to be an important factor in the
establishment of the current forest (Pederson, 2005). The site encompasses the fetch of
an eddy-flux covariance tower, installed in 1989 (Wofsy et al., 1993). To complement
the CO2 exchange data from the tower with ground-based measurements, 40 10 m radius
circular plots were established at random locations within 100 m segments of 8 500 m
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transects extending away from the tower to the northwest and southwest, the direction of
most prevailing wind (Figure 2.1). The plots were established in 1993, and annual
biometric measurement of forest carbon pools has taken place on them since 1998
(Barford et al., 2001). In 2001, 3 plots on the northwest edge of the site were flooded and
excluded from further measurements. Three of the original 40 plots lie on the harvest
site. The remaining 33 plots constitute the control site for this study.
The harvest site is located on the privately-owned Simmes Trust land, adjacent to
the control site. Before the harvest, the species composition on this site and the control
site were similar. The basal area of oak was not significantly different (P ≥ 0.05), at 12.4
m2/ha on the harvest site and 12.0 m2/ha on the control site, and oak was the most
prevalent species on both sites. There was also no significant difference in the
prevalence of hemlock (4.0 m2/ha on the harvest site and 5.9 m2/ha on the control site).
There were significant differences in the prevalence of maple (3.4 m2/ha on the harvest
site and 8.3 m2/ha on the control site) and beech (3.2 m2/ha on the harvest site and 0.19
m2/ha on the control site).
In addition to the three plots already established on this site, six plots were added
in 2000, one year before the harvest, to better capture the effects of the harvest on the
carbon dynamics of the forest. One of these plots was later abandoned because it was
unaffected by the harvest. The remaining eight plots were expanded to a radius of 15 m
to increase the area surveyed. Comparison of 2001 data from these expanded plots with
data based only on the inner 10 m radius plots showed that the expansion did not
introduce any significant bias (Figure 2.2).
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Figure 2.1 Map of control site and harvest site plots. The area betw
een Pierce road and the dashed line represents the planned harvest area.
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Figure 2.2 Comparison of biomass (MgC/ha) measurements in 2001 based on 10 m radius plots and expanded 15 m radius plots. Solid point indicates the mean biomass for the eight plots. The one to one line is shown. Figure modified from Curry (2002).
The harvest was carried out in the winter of 2001. The volume of wood removed
averaged 42.8 m3/ha, about 26% of the standing biomass. Of this, 36% was saw timber
and 64% was firewood (Curry, 2002). The volume removed was typical of harvests in
the North Quabbin region, which remove 44.7 m3/ha on average (Kittredge et al., 2003),
though a larger than average proportion of the wood from this harvest was sold as
firewood (Curry, 2002).
2.2 Carbon budget
An annual carbon budget for the harvest site from 2000 through 2006 was
constructed using data from measurements of the major carbon pools. Biometric
measurement of aboveground live and dead carbon pools was conducted, and where
direct measurements were not possible, fluxes were inferred from models and other
studies. Harvested wood was also accounted for in the carbon budget. The fluxes in
these pools were summed to examine the flux of carbon to the atmosphere on an annual
basis and cumulatively since the harvest.
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2.3 Live biomass measurements
In 1993, the diameter at breast height (DBH) of all trees in the control site plots ≥
10 cm DBH was recorded. These trees were measured again in 1998 and fitted with
stainless steel, spring-mounted dendrometer bands, which expand as the tree grows and
allow for precise growth measurements. In 2000 before the harvest, the same was done
for all trees ≥ 5 cm DBH in the harvest site plots. Smaller trees were included in the
harvest site measurements to capture a potentially different response of smaller trees to
the harvest. Every year since the dendrometers were installed, their expansion was
measured using calipers several times during the growing season. The expansion of the
dendrometers was converted to a change in DBH. From DBH, the woody biomass of
each tree was calculated using species specific allometric equations (Appendix A, Telfer,
1969; Grigal and Ohmann, 1977; Brenneman et al., 1978; Young et al., 1980; Jenkins et
al., 2003), and the carbon content was estimated as half of the woody biomass (Fahey et
al., 2005). The plots were surveyed for trees that had grown into the measured size
classes in 1999 on the control site and in 2003, 2004, and 2006 on both sites. The plots
were surveyed annually for trees that had died, determined by the absence of live foliage
at the peak of the growing season, and these trees were removed from the live tree
survey. These measurements were used to calculate the annual change in woody
biomass of living trees for the harvest site and control site due to growth, recruitment,
and mortality. Interannual and between-site comparisons were made using bootstrapped
95% confidence intervals (Efron and Tibhshirani, 1997).
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2.4 Woody debris survey
Coarse woody debris (CWD) was surveyed in all plots on the harvest site in 1999,
2001 (post-harvest), 2003 and 2006, and 27 of 33 control plots in 2000, 15 of 33 plots in
2003 and all 33 plots in 2006. The plots were surveyed for pieces of CWD within the
plot boundary ≥ 7.5 cm in diameter at the larger end. These pieces were classified by
type (log, log snag, whole tree snag, or stump, Appendix B.1). Logs ≤ 1 m in length were
excluded but stumps ≤ 1 m in height were not. Logs, log snags, and stumps were
measured so that their volumes could be calculated using equations from Harmon and
Sexton (1996, Appendix B.2). The DBH of whole tree snags was measured and used to
calculate their biomass using the same allometric equations used for live trees. Each
piece was also assigned to a decay class from 1 to 5, with 1 being the least decayed, using
criteria from Harmon and Sexton (Appendix B.3). Decay class specific densities
calculated for the Harvard Forest (Appendix B.4, Liu et al., 2006) were used to convert
volume to biomass for logs, log snags and stumps, and biomass to volume for whole tree
snags.
Fine woody debris (FWD, 2-7.5 cm in diameter) was surveyed on 10 m segments
of randomly placed line transects using the line-intercept method (Van Wagner 1968;
Brown 1974). The line-intercept measurements were extrapolated to volume per area
using an equation from Harmon and Sexton (1996, Appendix B.2) and volume was
converted to biomass using decay-class specific densities from Liu et al. (2006). In 2001,
26 segments were surveyed on each site. In 2003, 40 segments were surveyed on the
control site and 47 segments were surveyed on the harvest site. In 2006, 30 segments
were surveyed on the control site and 40 segments were surveyed on the harvest site.
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2.5 Coarse woody debris respiration model
Wood temperature and moisture content are important variables in determining
the rate of carbon loss from woody debris due to respiration (Liu et al., 2006).
Micrometeorological data collected at both the harvest and control sites in 2004 and 2005
showed between-site differences in log temperature, log moisture, and the diurnal range
of log temperatures (Table 2.1 and Figure 2.3, Curran, 2005). To account for site
differences in CWD pool dynamics due to differences in site conditions, a model for
hourly carbon flux from CWD to the atmosphere was developed for the periods between
CWD surveys. The CWD respiration model uses air temperature and decay class as
proxies for wood temperature and moisture to predict log transformed respiration rates (r2
= 0.32, Table 2.2). In this model, decay classes 4 and 5 were combined into decay class
V because the sample size in these classes was low, and decay classes 1 and 2 were
combined into decay class I because there was no significant difference in wood density
and moisture content for these classes (Liu et al., 2006). Respiration rates for downed
CWD (logs) were determined using the following equation:
ln R = Intercept + Ta + DIII + DV (1)
Where R is respiration (μg C g-1 C s-1), Ta is air temperature (K), and DIII and DV are
categorical parameters for decay classes III and V. Respiration rates for standing CWD
(snags and stumps) were estimated as 40% of downed CWD respiration rates (Erickson et
al., 1985; Mattson et al., 1987).
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On-site air temperature was measured at 30-minute intervals in 2004 and 2005 at
one harvest site plot and one control site plot. Measurements were made with YSI
(Yellow Springs, OH) 44032 Precision Thermistors housed inside Met One (Grant Pass
a) Measurement Mean 95% Confidence Int. Std. Dev. n Air Temperature (°C) 18.57 18.41 18.72 3.96 2522 Relative Humidity (%) 83.85 83.33 84.38 13.57 2521 Log Temperature Decay I (°C) 18.38 18.29 18.47 2.84 4184 Log Temperature Decay III (°C) 18.99 18.89 19.09 3.28 4184 Log Temperature Decay V (°C) 18.38 18.3 18.47 2.85 4184 Log Moisture Decay I (VWC) 0.54 0.51 0.57 0.05 12 Log Moisture Decay III (VWC) 0.46 0.44 0.48 0.03 12 Log Moisture Decay V (VWC) 0.80 0.80 0.80 0.00 12
b) Measurement Mean 95% Confidence Int. Std. Dev. n Air Temperature (°C) 17.97 17.84 18.10 3.81 3151 Relative Humidity (%) 89.03 88.65 89.40 10.73 3126 Log Temperature Decay I (°C) 17.50 17.42 17.59 2.90 4236 Log Temperature Decay III (°C) 17.20 17.13 17.28 2.44 4236 Log Temperature Decay V (°C) 17.17 17.09 17.25 2.60 4236 Log Moisture Decay I (VWC) 0.40 0.39 0.41 0.02 12 Log Moisture Decay III (VWC) 0.51 0.49 0.54 0.04 12 Log Moisture Decay V (VWC) 0.80 0.80 0.80 0.00 12
c) Measurement Mean 95% Confidence Int. Std. Dev. n p Air Temperature (°C) 0.44 0.42 0.47 41.55 2499 < 0.01 Relative Humidity (%) -4.51 -4.68 -4.35 -55.1 2417 < 0.01 Log Temperature Decay I (°C) 0.76 0.75 0.77 110.74 4006 < 0.01 Log Temperature Decay III (°C) 1.69 1.65 1.74 77.74 4006 < 0.01 Log Temperature Decay V (°C) 1.1 1.08 1.12 116.12 4006 < 0.01 Log Moisture Decay I (VWC) 0.14 0.12 0.17 12.6 11 < 0.01 Log Moisture Decay III (VWC) -0.05 -0.07 -0.04 -9.48 11 < 0.01 Log Moisture Decay V (VWC) 0 0 0 -- -- --
Table 2.1 Summary statistics for select micrometeorological measurements from July through September, 2004, at the (a) harvest site and (b) control site, and (c) the results of t-tests on the differences (harvest – control) between the two sites (Curran, 2005).
24
a) b)
c)
Figure 2.3 Comparison of log temperature over an eight day period in 2004 at the harvest and control sites for (a) decay class I, (b) decay class III, and (c) decay class V downed CWD (Curran, 2005).
Regression Coefficient
Standard Error
Intercept -28.672 0.066 Ta 0.078 0.003 DIII 0.422 0.058 DV 0.976 0.059
Table 2.2 Linear regression model predicting log transformed woody debris respiration rates (μg C g-1 C s-
1). Ta is air temperature (K). r2 = 0.32. DIII and DV are categorical parameters for decay class III and decay class V woody debris, respectively. OR) 076B-4 Aspirated Temperature Shields mounted 1.5 m off the ground at two points
10-15 m apart in each plot (Curran, 2005).
Micrometerological data from 2005 were used to create a linear regression model
that predicts hourly on-site air temperature from 2001 through 2006 using hourly air
temperature and incoming solar radiation measurements from the Fisher Meteorological
25
Station (harvest site r2 = 0.953, control site r2 = 0.987, Table 2.3). Because Fisher
Station data was not available before 2001, a second linear regression model was made
using micrometeorological data from 2004 to predict hourly control-site air temperature
using hourly air temperature and photosynthetically active radiation (PAR) measurements
from the HFEMS (r2 = 0.971, Table 2.4). This model was applied to both sites for 1999
and 2000, before the harvest site was logged. Modeled air temperatures that were outside
the range of air temperature values used in making the respiration model (262.85 –
305.75 K) were replaced with the maximum and minimum temperatures used in the
model.
Control Site Regression Coefficient
Standard Error Harvest Site
Regression Coefficient
Standard Error
Intercept 8.58 0.431 Intercept 42.96 1.26 Ta 0.969 0.00151 Ta 0.853 0.00431 ISR -0.00264 5.73E-05 ISR -0.000940 6.20E-05
Table 2.3 Linear regression model predicting hourly on-site air temperature for the control and harvest sites from hourly air temperature and solar radiation measured at the Fisher Meteorological Station. Control site r2 = 0.987, harvest site r2 = 0.953. Ta is air temperature (K). ISR is incoming solar radiation (W m-2).
Regression Coefficient
Standard Error
Intercept 25.67 0.812 Ta 0.908 0.00281 PAR 0.000392 2.48E-05
Table 2.4 Linear regression model predicting hourly on-site air temperature for the control site for 1999 and 2000 from hourly air temperature and photosynthetically active radiation (PAR) measured at the Harvard Forest Environmental Measurement Site. r2 = 0.971. Ta is air temperature (K). PAR is photosynthetically active radiation (μmol m-2 s-1). This model was applied to both sites for the two years before the harvest.
Hourly respiration rates were calculated from modeled hourly air temperature for
standing and downed CWD in each decay class on the two sites. These respiration rates
were applied to data from the CWD surveys to calculate the change in the sizes of the
pools due to respiration using the following equation:
26
pn = p0 (1 – R1) (1 – R2) … (1 – Rn) (2)
Where pn is the pool size (MgC/ha) after n hours, p0 is the initial pool size for the model
interval, and R1, R2,…Rn are the hourly respiration rates (converted to g C g-1 C hr-1).
At the start of model intervals which begin in the summer of CWD survey years,
the pool sizes were forced to the measured values from the corresponding survey. Four
times a year, one fourth of the mortality estimate (MgC/ha) from the following summer’s
mortality survey was added to the decay class I standing pool, because the mortality
estimate includes trees that died episodically between the previous and current summer
surveys.
To estimate the rate at which wood shifts to more decayed pools during the time
between two surveys, logs that were present in both surveys were identified and the
probabilities that they had shifted from class I to class III, from class I to class V, or from
class III to class V were calculated. The sample size of standing pieces that could be
identified in multiple surveys was too small to calculate these probabilities separately for
standing wood, so the shift probabilities for standing wood were estimated as 40% of the
probabilities for downed wood, based on the assumed difference in respiration rates. The
compound interest formula and between-survey shift probabilities were used to simulate
an annual shift of wood to more decayed pools as follows:
P = C (1 + r)t (3)
Where, as applied to the pool shifts, C is the size of the less decayed pool in the earlier
survey, P is the quantity of wood that shifted from the less decayed pool to the more
decayed pool between surveys, r is the annual shift rate, and t is the time in years
between surveys. The shift probabilities can be expressed in these terms:
27
S = 1 – P/C (4)
Where S is the calculated probability of wood shifting from one decay class to another.
The compound interest formula can then be solved for the annual shift rate:
r = (1 – S)1/t – 1 (5)
At the beginning of time intervals starting in the summer of non-survey years, the annual
shift rates were used to calculate the size of each pool shift using the following equation:
s = p – rp (6)
Where s is the amount of carbon shifted, r is the shift rate, and p is the size of the less
decayed pool. The shifts were subtracted from the less decayed pools and added to the
more decayed pools.
To estimate the rate at which wood shifted from standing to downed pools
between two surveys, the number of snags that were present in both the earlier and later
survey was determined and then used calculate the probability that a snag fell between
the two surveys. The compound interest formula was used again to convert this
probability to an annual shift rate. At the beginning of time steps starting in the summer
of non-survey years, the amount of carbon in falling snags of each decay class was
calculated using this rate. When a snag falls, it forms one or more logs, which become
part of the downed pool, and a stump that stays in the standing pool. To account for this,
a log to stump ratio was calculated as follows:
LS = (Vsnag – Vstump) / Vsnag (7)
Where LS is the log to stump ratio, Vsnag is the average volume of a log snag, and Vstump is
the average volume of a stump. This ratio was based only on control site data because
some stumps on the harvest site were created by cutting and may be different than stumps
28
created by trees falling due to natural causes. The standing to down shifts were
multiplied by the log to stump ratio before being subtracted from the standing decay class
pools and added to the corresponding downed decay class pools.
Pool dynamics were modeled from the summer of 2000 to the summer of 2006 on
the control site and the following equation was used to calculate the annual mass loss due
to respiration:
mi = pi – pi-1 + Mi (8)
Where mi is the mass lost due to respiration in year i, pi is the total size of the CWD pools
at the end of the last time step in year i, and Mi is the total addition from mortality in year
i. If the model is closely representing natural processes, the modeled pool sizes for the
summer of a survey year will be slightly larger than the measured pool sizes. The
difference accounts for the total mass lost to fragmentation and leaching between two
surveys.
Pool dynamics were modeled in the same way for the harvest site from the
summer of 1999 to the summer of 2000 and from the summer of 2001 to the summer of
2006. The harvest was treated as an instantaneous event occurring on January 1, 2001,
and the time between the summer of 2000 and the summer of 2001 was divided into a
153 day “pre-harvest” period before January 1, 2001, and a 212 day “post-harvest” period
after this date. To find the rate of pool shifts and standing to downed shifts for these
shortened time intervals, equation (5) was modified as follows:
r = (1 – S)d/τ – 1 (9)
29
Where r is the shift rate for the time interval, S is the shift probability, d is the length of
the shortened interval in days, and τ is the time in days between the two surveys used to
calculate S.
Inputs from mortality could not be determined for the harvest year because the
mortality survey included trees removed by the loggers. For mortality input dates in the
pre-harvest time period, the value for quarterly mortality inputs in 2000 was substituted,
and the mortality input value from 2002 was used for post-harvest inputs. Pre-harvest
pool dynamics were modeled using these shift rate and mortality input adjustments.
The harvest could not be simulated in the model, so modifications were made to
back calculate mass loss from respiration between the CWD survey in the summer of
2001 and the harvest. Starting with the 2001 measured pool sizes, the pool and standing
to down shift routines were run backwards by solving the system of equations given
below, which accounts for standing to down shifts being applied before pool shifts in the
regular model:
SIi = SIi-1 – SIi-1μ – SIi-1(1 – μ) σ13 – SIi-1 (1 – μ) σ 15
SIIIi = SIIIi-1 – SIIIi-1 μ – SIIIi-1(1 – μ) σ35 + SIi-1(1 – μ) σ13
SVi = SVi-1 – SVi-1 μ + SIi-1 (1 – μ) σ15 + SIIIi-1(1 – μ) σ35
DIi = DIi-1 + SIi-1 μ – (DIi-1 + SIi-1 μ) δ13 – (DIi-1 + SIi-1 μ) δ15
DIIIi = DIIIi-1 + SIIIi-1 μ – (DIIIi-1 + SIIIi-1 μ) δ35 + (DIi-1 + SIi-1 μ) δ13
DVi = DVi-1 + SVi-1 μ + (DIi-1 + SIi-1 μ) δ15 + (DIIIi-1 + SIIIi-1 μ) δ35 (13)
Where the measured values for decay class I, III, and V standing and downed pools, are
given by SIi, SIIIi, SVi, DIi, DIIIi, and DVi respectively, μ is the standing to downed shift rate,
σab is the shift rate from decay class a to decay class b for standing CWD, and δab is the
30
shift rate from decay class a to decay class b for downed CWD. The variables
subscripted i – 1 are the pool sizes for which the system was solved.
The change in the pools for each time step due to respiration was then calculated
using a variation on equation (4):
p0 = pn / (1 – R1) (1 – R2) … (1 – Rn) (11)
Where pn is the pool size at the end of the interval (which is the starting value in this
case), p0 is the pool size at the beginning of the interval, and R1, R2,…Rn are the hourly
respiration rates for the interval. The 2002 quarterly mortality value was subtracted from
the decay class I standing pool at points where mortality inputs would normally occur.
To estimate the error in the calculated pool sizes, a simulation of 1000 model runs
was developed. For each of the respiration regression parameters, a normal distribution
was created using the corresponding mean and standard error of each parameter. These
distributions were sampled 1000 times, then run in the model to produce a distribution of
pool sizes. The 95% confidence intervals of the respiration model were determined from
these pool size distributions. The same was done for the parameters in each of the
temperature regressions to determine the error due to this part of the model.
A model was not developed for FWD respiration, so annual FWD flux in the
carbon budgets is estimated as the change in biomass between surveys averaged over the
time between surveys. The first FWD measurement on both sites was in 2001. The 2001
harvest site FWD flux is estimated as the difference between the 2001 FWD
measurement (post-harvest) and the pre-harvest CWD measurement multiplied by the
average ratio of FWD to CWD on the control site (~25%). The 2001-2003 annual FWD
31
flux for the control site is used for the 2001 and 2000 flux in the control site carbon
budget and the 2000 FWD flux in the harvest site budget.
2.6 Belowground biomass and soil carbon fluxes
Fluxes in belowground biomass were estimated as 20% of fluxes in aboveground
biomass, as was done in previous carbon accounting on the control site (Barford et al.,
2001). This assumption is based on root excavation and measurement of a second-
growth forest in New Hampshire (Whittaker et al., 1974). To test the validity of this
assumption in the case of the harvested site, where stand composition has been altered,
the ratio of belowground to aboveground fluxes was calculated for this site using
allometric equations for belowground biomass (Jenkins et al., 2003). Soil carbon uptake
was estimated as 0.2 (± 0.1) Mg/ha yr based on a study of 14C residence time in soils at
the control site by Gaudinski et al. (2000).
2.7 Wood products accounting
Harvested wood was accounted for as either saw timber or firewood. The sawmill
that processed the wood from the harvest (Heyes Forest Products, Orange, MA) estimated
the merchantable volume of saw timber logs removed from the harvest site using an
international ¼” scaling rule and classified the higher quality saw timber by species and
grade, one (lowest quality) through four (highest quality). The lower quality saw timber
was designated for use in making industrial pallets or to be processed as pulp. The
carbon emissions resulting from fossil fuel use in harvesting and transport to the sawmill
were estimated using values from Raymer (2006) (all conversion factors in Appendix
32
C.1). The sawmill reported a 3% loss of saw timber biomass as sawdust and other
sawmill residues (Curry, 2002). Since most mill residues are burned for energy in the
place of oil, their heat content (Kuhns and Schmidt, 2003) and the carbon emissions of an
equivalent amount of oil (Aubé, 2001) were calculated. This value was subtracted from
the calculated carbon emissions for production of end use products from the saw timber
(Werner et al., 2005).
Descriptions of the end-use of saw timber by species and grade from Curry (2002)
were used to assign the wood products to end use categories given by Skog and
Nicholson (1998), for which they calculated the median life in use for the carbon in these
products. Equations from the HARVCARB model (Appendix C.2) developed by Row
and Phelps (1996) were used to determine the annual retirement of carbon from these
pools, accounting for recycling rates of paper and wood (EPA, 2005). Once transferred
out of use, the wood products were treated as municipal solid waste (MSW), a portion of
which is incinerated to produce energy while the rest is disposed of in a landfill (EPA,
2005). The annual landfill efflux of CO2 and CH4 due to the disposed wood products was
estimated using the LandGEM v.3.02 model (Alexander et al., 2005). Statistics for the
Martone Landfill in Barre, MA, a landfill operated by the major MSW management
service provider for the area (Waste Management of Central Massachusetts, West
Boylston, MA) were used as model parameters (Appendix C.3, DEP, 2006).
The volume of firewood removed in the harvest was measured in cords, but data
on the species composition of this wood was not available. To apply species specific
values for oven dry weight and heat content per cord (Kuhns and Schmidt, 2003), which
fell within a fairly small range for the major species on the harvest site, the pre-harvest
33
species composition of the harvest site was used to calculate a weighted average of these
values for the firewood. Biomass per cord was used to calculate the amount of carbon in
this pool, which was treated as a direct flux to the atmosphere. The use of wood for
heating offsets fossil fuel burning, so the emissions from burning heating oil (Aubé,
2001) and the relative efficiency of wood and oil heat were used to find the carbon
content of the offset emissions. This value was subtracted from the atmospheric flux
attributed to firewood.
2.8 Ecological measurements
The abundance of small trees and woody shrubs and leaf area index (LAI) were
measured at the harvest and control sites. Though they do not measure major carbon
pools, these measurements were useful in characterizing the effect of the harvest on
ecosystem function, quantifying recovery, and predicting the carbon dynamics of the
harvest site beyond the time frame of this study.
The DBH and species of all trees and woody shrubs 1-5 cm DBH on the harvest
site plots was recorded in November, 2006. The same was done on the control site plots
for trees and woody shrubs 1-10 cm DBH. These data and 2006 dendrometer data for
trees 5-10 cm DBH on the harvest site were used to calculate the frequency and basal
area of trees and woody shrubs 1-10 cm DBH by species on the two sites.
LAI was measured several times during the growing season in 1999, 2005, and
2006 using a LI-COR 2000 system (LI-COR Biosciences, Lincoln, NE). At each plot on
both the harvest and control sites, one measurement was made at the plot center and four
measurements were made 2 m from the plot center in each of the cardinal directions.
34
Readings from the outer ring of the sensor were masked to eliminate sunspots that may
have occurred in this ring. Measurements were processed using the program LI-COR
C2000. A LAI value for each plot was calculated as the average of the five subplot
readings.
35
3. Results
3.1 Live biomass
Carbon uptake in live biomass due to growth and recruitment of trees ≥ 10 cm
DBH was initially suppressed by the harvest, with additions due to growth and
recruitment on the harvest site totaling 1.11 ± 0.39 MgC/ha in 2001 compared to 2.01 ±
0.26 MgC/ha on the control site (numbers of variance give bootstrapped 95% confidence
intervals, Figure 3.1). In contrast, growth and recruitment were not significantly different
(P = 0.10) between the two sites in the year before the harvest. By 2004 and 2005,
growth and recruitment on the harvest site increased to 2.39 ± 0.35 and 2.62 ± 0.35,
respectively. These fluxes were not significantly different from live biomass additions on
the control site, where growth fluctuated due to climatic variability and periodic
disturbance. Mortality was high in the harvest year at 13.95 ± 9.43 MgC/ha, because
trees removed in the logging operation are included in this figure. Mortality on the
harvest site and control site were not significantly different in subsequent years.
The average percent increase in biomass of oaks on the harvest site was 11 ± 3% and
19 ± 4% compared to 22 ± 3% and 27 ± 3% on the control site in 2001 and 2002,
respectively, but recovered to values similar to those on the control site in following years
(Figure 3.2a), indicating that the harvest had a short-term growth suppression effect on oaks.
Red maple and birch, which are early successionals, showed evidence of growth release
almost immediately after the harvest, increasing significantly in average percent growth
between 2000 and 2002 and surpassing average percent growth for the same species groups
on the control site in following years (Figures 3.2b and c). Hemlock, a late successional,
increased in average percent growth (7 ± 2% to 12 ± 2%) between 2001 and 2002 and
36
a)
Figure 3.1 Gross annual fluxes in live biom
ass for 2000-2006 on the (a) control site and (b) harvest site. Green bars
represent growth of trees ≥ 10 cm
DB
H and blue bars represent recruitm
ent of trees into the measured diam
eter class. R
ed bars represent mortality. There is a break in the negative y-axis on the harvest site figure to display 2001
mortality. Error bars show
bootstrapped 95% confidence intervals of grow
th + recruitment and m
ortality .
b)
37
remained greater at the harvest site from 2003 to 2006, though the between-site difference
was only significant in 2004 and 2005. The average percent growth for beech, another late
successional, increased between 2001 and 2002 (14 ± 4% to 24 ± 5%) on the harvest site, but
a between-site comparison was not possible because beech is infrequent on the control site.
a) b)
c) d)
Figure 3.2 Mean percent growth per tree for (a) oak, (b) maple, (c) birch, and (d) hemlock on the harvest and control sites from 2000 to 2006. Error bars show bootstrapped 95% confidence intervals.
Following the harvest, uptake was greater for trees in smaller diameter classes on
a per area basis (Figure 3.3). In 2006, trees 10-30 cm diameter at breast height (DBH)
accounted for 59 ± 15% of uptake on the harvest site and 33 ± 7% of uptake on the
control site. The number of trees per hectare 10-20 and 20-30 cm DBH was not
significantly higher on the harvest site in any of the study years (0.28 < P < 0.88), so this
difference in per area uptake is attributable mostly to growth release and not to
recruitment or pre-harvest differences in stand structure. The average uptake for trees in
38
the 10-20 cm diameter class on the harvest site increased from 0.84 ± 0.19 kgC tree-1 yr-1
in 2000 to 1.84 ± 0.35 kgC tree-1 yr-1 in 2002, compared to 0.85 ± 0.13 and 1.05 ± 0.19
kgC tree-1 yr-1 on the control site, and was significantly higher on the harvest site than the
control site from 2003 through 2006 (Figure 3.4a). Trees in the 20-30 cm diameter class
showed a similar growth increase between 2000 and 2003, going from 2.01 ± 0.49 to 4.48
± 0.64 kgC tree-1 yr-1 compared to 2.60 ± 0.31 and 2.86 ± 0.47 kgC tree-1 yr-1 on the
control site (Figure 3.4b). Growth was higher on the harvest site for trees 30-40 cm DBH
in 2003 through 2006, though the difference was only significant in 2005 and was
proportionally smaller than the between-site difference for trees in the smaller diameter
classes (Figure 3.4c). Per tree uptake for trees 40 cm DBH and larger was significantly
higher on the control site before the harvest, 11.25 ± 2.11 compared to 6.79 ± 1.01 kgC
tree-1 yr-1. The between-site difference in this diameter class was not significant for 2001
through 2006, but harvest site uptake increased significantly over this interval to 14.1 ±
3.18 kgC tree-1 yr-1 (Figure 3.4d).
b) a)
Figure 3.3 Annual increase in aboveground live biomass (MgC/ha) due to growth and recruitment in 2000-2006 on the (a) control site and (b) harvest site, partitioned by diameter class in 10 cm increments. Bottom sections represent trees 40 cm DBH and larger and top sections represent trees 10-20 cm DBH.
39
a) b)
c) d)
Figure 3.4 Mean annual growth by diameter class per tree (kgC/tree), 2000 – 2006, on the harvest and control sites for trees (a) 10 – 20 cm DBH, (b) 20 – 30 cm DBH, (c) 30 – 40 cm DBH and (d) > 40 cm DBH from 2000 to 2006. Error bars show bootstrapped 95% confidence intervals.
b) a)
Figure 3.5 Distribution of aboveground biomass (MgC/ha) of major species and species groups by diameter class in 10 cm increments on the (a) control site and (b) harvest site before the harvest (2000), shortly after the harvest (2001), and five years after the harvest (2006). Bottom sections represent trees ≥ 40 cm DBH and top sections represent trees 10-20 cm DBH.
The diameter class distributions of individual species may confound these
between-site differences in growth by species and size class. The majority of the biomass
(92 ± 18%) of red maple, the species showing the greatest growth increase, was in trees <
40
30 cm DBH, the size classes showing the greatest growth increase. Only 39 ± 26% of
oak was in these diameter classes (Figure 3.5).
Belowground biomass on the harvest site, as estimated with allometric equations
from Jenkins et al. (2003), equaled 19.4% of aboveground biomass, which agreed well
with the estimate that belowground biomass equals 20% of aboveground biomass from
Whittaker et al. (1974). The 20% estimate was used to be consistent with previous
carbon accounting on the study site (Barford et al., 2001).
3.2 Woody debris
CWD biomass was higher on the harvest site than on the control site before the
harvest, though this difference was not significant (Figure 3.6). Following the harvest,
the carbon stored in CWD biomass was 13.32 ± 3.32 MgC/ha, which was not
significantly different than the 1999 harvest site measurement (9.64 ± 3.32 MgC/ha) but
was significantly higher than the 2000 control site measurement (5.21 ± 1.39 MgC/ha).
Though the decrease in harvest site CWD between 2001 and 2006 was not significant,
2006 was the first survey year since the harvest for which there was not a significant
between-site difference in CWD biomass. On the control site, CWD biomass did not
change significantly over the study period, and CWD volume remained nearly constant
over the study period on both sites (Figure 3.7).
41
a) b)
Figure 3.6 Biomass (MgC/ha) of coarse woody debris in survey years on the (a) control and (b) harvest site plots. Control site data is only available for 27 of 33 plots in 2000 and 15 of 33 plots in 2003, so biomass calculated for these subsamples is shown in years when more plots were surveyed. Eight plots were surveyed in all years on the cut site, but the survey in 1999 was done before the plots were expanded from a 10 m to a 15 m radius. Error bars show bootstrapped 95% confidence intervals.
a) b)
Figure 3.7 Volume (m3/ha) of coarse woody debris in survey years on the (a) control and (b) harvest site plots. Control site data is only available for 27 of 33 plots in 2000 and 15 of 33 plots in 2003, so biomass calculated for these subsamples is shown in years when more plots were surveyed. Eight plots were surveyed in all years on the cut site, but the survey in 1999 was done before the plots were expanded from a 10 m to a 15 m radius. Error bars show bootstrapped 95% confidence intervals. The volume of CWD in less decayed classes (1 & 2) on the harvest site increased
from 10.02 ± 7.77 to 43.96 ± 14.16 m3/ha between 1999 and 2001 (Figure 3.8b). The
decay class distribution of harvest site CWD volume between 2001 and 2003 was nearly
static. Between 2003 and 2006 there was a decrease in the volume of decay classes 1 and
2 from 42.60 ± 11.47 to 5.62 ± 3.44 m3/ha and a corresponding increase in decay class 3
volume from 11.31 ± 4.35 to 40.06 ± 15.82 m3/ha. There were no significant changes in
the decay class distribution on the control site between 2000 and 2006 (Figure 3.8a). The
volume of harvest site CWD in snags decreased from 27.27 ± 13.11 to 8.60 ± 2.89 m3/ha,
42
while the volume of CWD in stumps increased from 0.76 ± 0.93 to 3.59 ± 0.97 m3/ha
after the harvest, but the harvest site CWD type distribution didn’t change significantly
between 2001 and 2006 (Figure 3.9a). The CWD type distribution did not change
significantly on the control site (Figure 3.9b).
a) b)
Figure 3.8 Volume of CWD by decay class (m3/ha) in survey years on the (a) control site and (b) harvest site. Control site data is the 15 plots for which data is available in all survey years. Eight plots were surveyed in all years on the cut site, but the survey in 1999 was done before the plots were expanded from a 10 m to a 15 m radius. Error bars show bootstrapped 95% confidence intervals.
a) b)
Figure 3.9 Volume (m3/ha) of CWD by type in survey years on the (a) control site and (b) harvest site. Control site data is based on the 15 plots for which data is available in all survey years. Eight plots were surveyed in all years on the cut site, but the survey in 1999 was done before the plots were expanded from a 10 m to a 15 m radius. Error bars show bootstrapped 95% confidence intervals.
Carbon storage in fine woody debris (FWD) on the harvest site in 2001, 7.81 ±
2.77 MgC/ha, was much higher than on the control site, 1.05 ± 0.40 MgC/ha, and was
also significantly higher in 2003 and 2006. There was no significant change in this pool
between years on either site (Figure 3.10).
43
b) a)
Figure 3.10 Biomass of fine woody debris (MgC/ha) in survey years on the (a) control site and (b) harvest site and the number of segments surveyed. Error bars show bootstrapped 95% confidence intervals.
3.3 Coarse woody debris respiration
Mean modeled site temperatures from 2001 through 2006 were 9.46 ± 0.08°C for
the harvest site and 7.52 ± 0.09°C for the control site, which resulted in higher mean
CWD respiration rates by decay class on the harvest site. Respiration rates based on
modeled site-specific temperatures were similar to those calculated by Liu et al. (2006)
which were based on a single mean air temperature (7.88°C) measured at the HFEMS
(Table 3.1). The average respiration rate, weighted by decay class and standing-downed
biomass distribution, was significantly higher on the harvest site (0.0667 ± 0.0008 gC g-
1C yr-1) than on the control site (0.0426 ± 0.0009 gC g-1C yr-1) before the harvest because
the largest portion of the CWD pool on the harvest site (37%) was decay class V and
downed which has the highest decay rate, while the largest CWD pool on the control site
(39%) was decay class I and standing, which has the lowest decay rate. The weighted
average respiration rates for downed CWD from 2001 to 2006 were similar to those
reported by Liu et al. (2006) for the harvest and control sites, though they resulted in
slightly different estimates for the lifetime of downed CWD. Even though the harvest
44
decreased the pool size of more decayed classes and increased the pool size of less
decayed classes, weighted average respiration rate was still higher on the harvest site
than on the control site for 2001 through 2006, 0.0604 ± 0.0004 compared to 0.0446 ±
0.0003 gC g-1C yr-1 (Table 3.2, Figure 3.11). This is due to the decrease in standing
CWD, which decays more slowly, after the harvest.
Control Site Harvest Site Liu et al., 2006 Decay Class I 0.0475 ± 0.0003 0.0529 ± 0.0003 0.06 ± 0.02 Decay Class III 0.0725 ± 0.0005 0.0806 ± 0.0005 0.10 ± 0.03 Decay Class V 0.1261 ± 0.0008 0.1403 ± 0.0008 0.14 ± 0.07
Table 3.1 Average annual respiration rate (g C g-1 yr-1) for downed coarse woody debris. Control site and harvest site averages are the mean of hourly respiration rates calculated for each decay class using modeled on-site air temperature from 2001 – 2006. Decay rates reported by Liu et al. (2006) were calculated using the mean annual air temperatures recorded from 1992 – 2003 at the HFEMS. a) Hourly model, 2001-2006 b) Liu et al. (2006) Harvest Control Harvest Control Downed CWD decay rate (yr-1) 0.0752 ± 0.0005 0.0739 ± 0.0005 0.08 ± 0.04 0.09 ± 0.04 Total CWD decay rate (yr-1) 0.0604 ± 0.0004 0.0446 ± 0.0003 N/A N/A Downed CWD lifetime (yr) 13 14 13 11 Total CWD lifetime (yr-1) 17 22 N/A N/A
Table 3.2 Average respiration rates, weighted by decay class and standing-downed distribution, and coarse woody debris lifetimes calculated by (a) the CWD hourly respiration model from 2001 – 2006 and (b) Liu et al. (2006), who used the mean annual air temperatures recorded from 1992 – 2003 at the HFEMS.
Figure 3.11 Annual CWD respiration rate (gC g-1C yr-1) from 2000 – 2006 on the harvest site (red) and 2001 – 2006 on the control site (black) weighted by decay class and standing-downed biomass distribution. Error bars show 95% confidence intervals.
45
CWD mass loss due to respiration from 2001 to 2006 totaled 4.7 MgC/ha on the
harvest site and 1.6 MgC/ha on the control site (Figure 3.12). Mass losses due to
fragmentation and leaching on the control site were 0.42 ± 1.34 MgC/ha between 2000
and 2003 and 0.72 ± 2.37 MgC/ha between 2003 and 2006, the equivalent of 53% and
90% of the losses due to respiration, respectively. On the cut site, fragmentation and
leaching accounted for a loss of 1.07 ± 5.31 MgC/ha between 2003 and 2006, 45% of
losses to respiration. For the interval from 1999 to 2003 the model yielded a CWD
biomass value that was smaller than what was measured so no fragmentation and
leaching term could be calculated.
Figure 3.12 Modeled (triangles) and measured (squares) coarse woody debris pool sizes. Squares show measured CWD pool sizes (MgC/ha) and triangles show pool sizes predicted by the CWD respiration model from 1999 – 2006 on the harvest site (red) and 2000 – 2006 on the control site (black). In years for which there is a modeled value and a measured value, the difference between the two is assumed to be the mass lost to fragmentation and leaching between the two surveys. Error bars show 95% confidence intervals of the respiration model. Error from the temperature models was less than 13% of the error from the respiration model in all years. 3.4 Wood products
The high proportion of harvested wood sold as firewood resulted in a large short-
term flux of carbon to the atmosphere of 381 MgC due to fuelwood burning, including
sawmill residues as fuelwood. If this wood is considered as a substitute for fossil fuel
46
burning, 217 MgC (58%) in emissions are offset (Table 3.3). Carbon emission from
fossil fuel burning in harvesting and transport of saw timber and firewood (3.9 MgC) was
small relative to the other short-term fluxes. Emissions from timber and pulp processing
totaled 70 MgC, with the largest contributions coming from the processing of wood used
in flooring (23 MgC), paper (20 MgC), furniture (15 MgC), and construction beams (13
MgC).
MgC MgC/ha Harvesting & Transport -3.86 -0.09 Timber & Pulp Processing -70.03 -1.62 Sawdust & Sawmill Residue Burning -4.87 -0.11 Fossil Fuel Offset 3.18 0.07 Net Flux -1.66 -0.04 Firewood Burning -375.96 -8.72 Fossil Fuel Offset 214.16 4.97 Net Flux -161.81 -3.75 Total Short-term Flux -237.39 -5.51
Table 3.3 Short-term fluxes of carbon to the atmosphere due to fossil fuel use in logging, transport, and production of wood products and burning of harvested wood with fossil fuel offsets. Negative values represent a net flux of carbon to the atmosphere.
Two-thirds of the sawtimber (105 MgC) entered end-use pools with a median life
in use (MLU) of 30 years or more (Table 3.4). As predicted by the HARVCARB model
using MLUs adjusted to account for recycling, 73-98% of wood products in these
categories will be in use 25 years after production and 28-96% will be in use 50 years
after production (Figure 3.13). In the five years following the harvest, the model
estimated that the majority (83%) of the paper made from harvested pulpwood and 9.2 of
the 29.9 MgC (31%) used to make industrial pallets were disposed, but no more than 6%
of wood in the other end-use categories was disposed. The total estimated disposal from
2001 through 2006 was 20.2 MgC (Figure 3.14). Of the wood products disposed, 2.8
MgC were incinerated and 17.4 MgC entered landfills.
47
Pool MgC MgC/ha MLU Adjusted MLU Residential Construction 48.23 1.12 100 110 Non-Residential Construction 31.31 0.73 67 74 Furniture 25.69 0.6 30 33 Manufacturing 11.69 0.27 12 13 Industrial Pallets 29.89 0.69 6 7 Paper 11.43 0.27 1 2 Total 158.24 3.68
Table 3.4 Initial pool sizes of carbon from saw timber removed from the harvest site into end-use categories based on description of timber use by species and grade for wood from this harvest (Curry 2002). Median life in use (MLU) determined for each category by Skog and Nicholson (1998), is given with the adjusted MLU, which is increased to account for recycling.
Figure 3.13 Proportion of original wood products still in use by end-use category from 0 to 80 years after production as projected by the HARVCARB model (Row and Phelps, 1996) using median life in use times from Skogg and Nicholson (1998), adjusted to account for recycling.
Figure 3.14 Carbon storage in wood products originating from the 2001 harvest from 2001 to 2006 as projected by the HARVCARB model (Row and Phelps, 1996) using median life in use times from Skogg and Nicholson (1998), adjusted to account for recycling.
48
As the total estimated landfill input from harvested wood products increases, a
portion of the carbon from the harvested wood will be returned to the atmosphere through
the efflux of landfill gases. The LandGEM model estimated that the annual rate of
landfill gas efflux due to inputs of wood products from the harvest site will increase from
0.03 MgC/yr in 2002 to 0.23 MgC/yr by 2010. About 50% of this carbon by mass is
decayed through anaerobic respiration and returned to the atmosphere as methane. A flux
of 0.23 MgC to the atmosphere as landfill gases has the same global warming potential
(GWP) as a flux of 1.01 MgC as CO2 (Figure 3.15).
Figure 3.15 Annual landfill gas efflux (MgC), shown by the solid line, due to disposed wood products from the 2001 harvest as modeled using LandGEM v3.02 with model parameters for the Martone Landfill in Barre, MA. Efflux rates are shown from the time of the harvest through 2010, when the landfill is scheduled to close. The dashed line shows the carbon content of a mass of CO2 with a global warming potential equal to the annual landfill gas efflux.
Between 2001 and 2006, LandGEM estimated that, based on the rate of disposal
predicted by HARVCARB and the rate of decay in this landfill, 0.46 MgC from the
harvest would be returned to the atmosphere as landfill gases with a GWP equivalent to
1.97 MgC as CO2. This is not significant on a per hectare basis, so it is not included in
the carbon budget. The Martone Landfill closes in 2010, so the combined effects of the
disposal rate and efflux rate could not be modeled past this date, but disposed wood
49
products will have a significant effect on the carbon dynamics of the system beyond this
10-year time scale. Under the conditions estimated for disposed wood products from this
harvest, 1 MgC disposed in 2001 will result in the efflux of 0.34 MgC in landfill gases
with a GWP equivalent to 1.46 MgC as CO2 by 2100 (Figure 3.16).
Figure 3.16 Cumulative efflux of 1 MgC in landfill waste, 2001-2100. The solid line shows the cumulative carbon content of landfill gas efflux due to wood products containing 1 MgC disposed of in 2001 over 100 hundred years as predicted by LandGEM v3.02. The dashed line shows the carbon content of a mass of CO2 with a global warming potential equivalent to the cumulative landfill gas efflux. 3.5 Carbon budget
There was a large flux of 15.5 ± 10.7 MgC/ha out of the live carbon pools in 2001
due to the harvest (Table 3.5). The aboveground portion of this went into CWD (4.2 ±
3.7 MgC/ha, calculated using the CWD respiration model), FWD (5.5 ± 4.6 MgC/ha),
wood products (3.7 MgC/ha), and fuelwood (8.8 MgC/ha) pools. The estimated sizes of
these smaller pools total 22.1 MgC/ha, which agrees reasonably well with the
aboveground mortality estimate (14.0 ± 11.2 MgC/ha, 18.4 ± 11.3 MgC/ha if trees 5-10
cm DBH are included). After short-term fluxes are accounted for, harvested wood did
not contribute to the net annual atmosphere-biosphere carbon flux for the harvest site
because landfill gas efflux and MSW incineration is not significant over the time interval
50
included in the carbon budget. In 2002 and 2003, the fluxes of carbon to the atmosphere
due to woody debris decay and to the ecosystem due to tree growth nearly balanced out,
resulting in net carbon fluxes close to zero. On-site carbon uptake increased to a level
similar to that of the control site between 2003 and 2004 (Table 3.6), though this increase
can be attributed almost entirely to the stabilization of the FWD pool, for which
uncertainty is high. The harvest site was a net carbon sink with a magnitude similar to
the control site from 2004 to 2006.
The loss of carbon from the harvest site in 2001 was counterbalanced by uptake in
subsequent years, making the on-site carbon pools a small net carbon sink of 1.4 MgC/ha
over the five year period since the harvest. Fluxes in the aboveground carbon pools,
which were measured directly, show the same pattern of decrease and recovery following
the harvest as the overall on-site carbon dynamics (Table 3.7). However, these pools are
a net carbon source in 2002 and 2003 while the site as a whole is close to neutral.
Considering on-site carbon pools, short-term carbon fluxes, fossil fuel offsets, and carbon
storage in wood products together, the system is a net carbon sink of 8.4 MgC/ha over the
five year period (Figure 3.16). The magnitude of this sink is still about half that of the
control site, which sequestered a total of 16.4 MgC/ha over the same time interval.
51
Figure 3.16 Annual carbon flux (MgC/ha) from the year before the 2001 harvest through 2006 in relation to the atmosphere for the control site and the harvest site including carbon in harvested wood. Negative values indicate a net flux to the atmosphere. The dashed lines show the mean annual net fluxes over the time period for the two sites.
52
2000 2001
2002 2003
2004 2005
2006 T
otal, 2001 – 2006 L
ive Biom
ass A
boveground
Grow
th 1.5±0.5
1.1±0.4 2.1±0.3
2.1±0.3 2.4±0.4
2.6±0.4 2.5±0.4
12.8
Mortality
-0.3±0.6 -14.0±11.2
-0.1±0.2 -0.3±0.3
-0.6±1.2 -0.2±0.2
-0.3±0.7 -15.5
Below
ground
Grow
th 0.3±0.1
0.2±0.1 0.4±0.1
0.4±0.1 0.5±0.1
0.5±0.1 0.5±0.1
2.5
Mortality
-0.1±0.1 -2.8±2.2
0.0±0.0 -0.1±0.1
-0.1±0.2 0.0±0.0
-0.1±0.1 -3.1
Subtotal 1.6±0.5
-15.5±10.7 2.4±0.3
2.1±0.4 2.2±1.2
2.9±0.4 2.6±0.8
-3.3 W
oody Debris (W
D)
Coarse W
D
M
ortality Input 0.3±0.6
4.2±3.7 0.1±0.2
0.3±0.3 0.6±1.2
0.2±0.2 0.3±0.7
5.7
Respiration
-0.6±1.8 -0.7±1.9
-0.8±2.5 -0.8±1.6
-0.7±2.2 -0.8±1.8
-0.9±1.2 -4.7
Fine WD
0.3±0.6††
5.5±4.6† -1.8±2.4*
-1.8±2.4* 0.2±1.2*
0.2±1.2* 0.2±1.2*
2.5 Subtotal
0.0 9.0
-2.5 -2.3
0.1 -0.4
-0.4 3.5
Net Soil C
Flux 0.2±0.1
0.2±0.1 0.2±0.1
0.2±0.1 0.2±0.1
0.2±0.1 0.2±0.1
1.2 O
n-site total 1.8
-6.3 0.1
0.0 2.5
2.7 2.4
1.4 H
arvested Wood
Fossil Fuel U
se
-1.7
-1.7
Wood Products
3.7
-0.1 -0.1
-0.1 -0.1
-0.1 3.2
Fuel W
ood
8.8
8.8
Fuel Wood B
urned
-8.8
-8.8
Landfill Input
0.1
0.1 0.1
0.1 0.1
0.5
Fossil Fuel Offsets
5.0
5.0
Subtotal
7.0 0.0
0.0 0.0
0.0 0.0
7.0 A
nnual Total
1.8 0.7
0.1 0.0
2.5 2.7
2.4 8.4
T
able 3.5 Annual fluxes (M
gC/ha) in carbon pools on the harvest site, 2000-2006. Positive annual totals indicate flux from
the atmosphere to terrestrial
carbon pools. Grow
th in aboveground live biomass includes inputs from
recruitment and is calculated for trees ≥ 10 cm
DB
H. B
reakdown of coarse w
oody debris flux w
as estimated using the w
oody debris respiration model. B
elowground live biom
ass fluxes are estimated as 20%
of aboveground fluxes based on W
hittaker et al. (1974). Soil carbon flux is estimated based on m
easurement of the residence tim
e of 14C in H
arvard Forest soils (Gaudinski et al. 2000).
*Multiyear averages. †Estim
ated as the difference between m
easured post-harvest FWD
and 25% of pre-harvest C
WD
because there was no reference FW
D
measurem
ent. ††Estimated as equal to the 2001 – 2003 annual FW
D flux on the control site. B
ootstrapped 95% confidence intervals are given w
here they could be calculated.
53
2000
20
01
2002
20
03
2004
20
05
2006
T
otal
, 200
1 –
2006
L
ive
Bio
mas
s A
bove
grou
nd
G
row
th
1.9±
0.2
2.0±
0.3
2.6±
0.3
2.2±
0.3
2.0±
0.3
2.3±
0.3
2.5±
0.3
13.6
Mor
talit
y -0
.2±0
.2
-0.1
±0.1
-1
.3±1
.2
-0.4
±0.5
-0
.6±0
.6
-1.1
±0.9
-0
.5±0
.5
-4.0
B
elow
grou
nd
G
row
th
0.4±
0.0
0.4±
0.0
0.5±
0.1
0.4±
0.1
0.4±
0.1
0.5±
0.1
0.5±
0.1
2.7
M
orta
lity
0.0±
0.0
0.0±
0.0
-0.3
±0.3
-0
.1±0
.1
-0.1
±0.1
-0
.2±0
.2
-0.1
±0.1
-0
.8
Subt
otal
2.
1±0.
2 2.
3±0.
3 1.
5±0.
5 2.
1±0.
4 1.
7±0.
3 1.
5±0.
4 2.
4±0.
3 11
.5
Woo
dy D
ebri
s (W
D)
Coa
rse
WD
Mor
talit
y In
put
0.
1±0.
1 1.
3±1.
2 0.
4±0.
5 0.
6±0.
6 1.
1±0.
9 0.
5±0.
5 4.
0
Res
pira
tion
-0
.2±0
.9
-0.3
±1.7
-0
.3±2
.3
-0.3
±1.1
-0
.3±2
.1
-0.2
±3.0
-1
.6
Fine
WD
0.3±
0.6†
0.
3±0.
6*
0.3±
0.6*
0.
1±0.
6*
0.1±
0.6*
0.
1±0.
6*
1.2
Subt
otal
0.2
1.3
0.4
0.4
0.9
0.4
3.6
Soil
Car
bon
0.
2±0.
1 0.
2±0.
1 0.
2±0.
1 0.
2±0.
1 0.
2±0.
1 0.
2±0.
1 1.
2 T
otal
2.
1**
2.7
3.0
2.7
2.3
2.6
3.0
16.3
T
able
3.6
Ann
ual f
luxe
s (M
gC/h
a) in
car
bon
pool
s at t
he c
ontro
l site
, 200
0-20
06.
Posi
tive
annu
al to
tals
indi
cate
flux
from
the
atm
osph
ere
to te
rres
trial
car
bon
pool
s. G
row
th in
abo
vegr
ound
live
bio
mas
s inc
lude
s inp
uts f
rom
recr
uitm
ent a
nd is
cal
cula
ted
for t
rees
≥ 1
0 cm
. B
reak
dow
n of
coa
rse
woo
dy d
ebris
flux
was
es
timat
ed u
sing
the
woo
dy d
ebris
resp
iratio
n m
odel
. B
elow
grou
nd li
ve b
iom
ass f
luxe
s are
est
imat
ed a
s 20%
of a
bove
grou
nd fl
uxes
bas
ed o
n W
hitta
ker e
t al.
(197
4).
Soil
carb
on fl
ux is
est
imat
ed b
ased
on
mea
sure
men
t of t
he re
side
nce
time
of 14
C in
Har
vard
For
est s
oils
(Gau
dins
ki e
t al.
2000
). *
Mul
tiyea
r ave
rage
s.
**Ed
dy fl
ux m
easu
rem
ent (
Bar
ford
et a
l. 20
01).
†Es
timat
ed a
s equ
al to
the
2001
– 2
003
aver
age
annu
al fl
ux b
ecau
se 2
001
is th
e fir
st m
easu
rem
ent y
ear f
or
FWD
. B
oots
trapp
ed 9
5% c
onfid
ence
inte
rval
s are
giv
en w
here
they
cou
ld b
e ca
lcul
ated
.
54
a) Harvest Site
2000 2001
2002 2003
2004 2005
2006 T
otal, 2001 – 2006 L
ive Biom
ass
Grow
th 1.5±0.5
1.1±0.4 2.1±0.3
2.1±0.3 2.4±0.4
2.6±0.4 2.5±0.4
12.8
Mortality
-0.3±0.6 -14.0±11.2
-0.1±0.2 -0.3±0.3
-0.6±1.2 -0.2±0.2
-0.3±0.7 -15.5
Subtotal 1.2±0.5
-12.9±9.8 2.0±0.2
1.8±0.3 1.8±1.0
2.4±0.3 2.2±0.6
-2.7 W
oody Debris (W
D)
Coarse W
D
M
ortality Input 0.3±0.6
4.2±3.7 0.1±0.2
0.3±0.3 0.6±1.2
0.2±0.2 0.3±0.7
5.7
Respiration
-0.6±1.8 -0.7±1.9
-0.8±2.5 -0.8±1.6
-0.7±2.2 -0.8±1.8
-0.9±1.2 -4.7
Fine WD
0.3±0.6†
5.5±4.6†† -1.8±2.4*
-1.8±2.4* 0.2±1.2*
0.2±1.2* 0.2±1.2*
2.5 Subtotal
0.0 9.0
-2.5 -2.3
0.1 -0.4
-0.4 3.5
Total
1.2 -3.9
-0.5 -0.5
1.9 2.0
1.8 0.8
b) Control Site
2000 2001
2002 2003
2004 2005
2006 T
otal, 2001 – 2006 L
ive Biom
ass
Grow
th 1.9±0.2
2.0±0.3 2.6±0.3
2.2±0.3 2.0±0.3
2.3±0.3 2.5±0.3
13.6
Mortality
-0.2±0.2 -0.1±0.1
-1.3±1.2 -0.4±0.5
-0.6±0.6 -1.1±0.9
-0.5±0.5 -4.0
Subtotal 1.7±0.2
1.9±0.3 1.3±0.4
1.8±0.3 1.4±0.2
1.2±0.3 2.0±0.3
9.6 W
oody Debris (W
D)
Coarse W
D
M
ortality Input
0.1±0.1 1.3±1.2
0.4±0.5 0.6±0.6
1.1±0.9 0.5±0.5
4.0
Respiration
-0.2±0.9
-0.3±1.7 -0.3±2.3
-0.3±1.1 -0.3±2.1
-0.2±3.0 -1.6
Fine WD
0.3±0.6† 0.3±0.6*
0.3±0.6* 0.1±0.6*
0.1±0.6* 0.1±0.6*
1.2 Subtotal
0.2
1.3 0.4
0.4 0.9
0.4 3.6
Total
2.1
2.6 2.2
1.8 2.1
2.4 13.2
Table 3.7 A
nnual fluxes (MgC
/ha) in on-site, aboveground carbon pools on the (a) harvest site and (b) control site, 2001-2006. Grow
th in aboveground live biom
ass includes inputs from recruitm
ent. Breakdow
n of coarse woody debris flux w
as estimated using a w
oody debris respiration model. *M
ultiyear averages. †Estim
ated as equal to the 2001 – 2003 average annual FWD
flux for the control site because a measurem
ent is not available. ††Estimated as the
difference between m
easured post-harvest FWD
and 25% of pre-harvest C
WD
because there was no reference FW
D m
easurement. B
ootstrapped 95%
confidence intervals are given where they could be calculated.
55
3.6 Sapling abundance
The frequency of trees and woody shrubs 1-10 cm DBH was not significantly
different between the two sites, with 1239 ± 293 stems/ha on the control site compared to
1135 ± 554 stems/ha on the harvest site, but the basal area of this size class was
significantly higher on the harvest site at 2.89 ± 0.84 m2/ha than on the control site at
1.65 ± 0.32 m2/ha (Figure 3.17). Woody shrubs were more frequent on the control site at
453 ± 218 stems/ha than on the harvest site at 60 ± 49 stems/ha. Beech was the most
frequent species in this diameter class on the harvest site with 292 ± 99 stems/ha, and was
significantly more frequent than oak with 21 ± 32 stems/ha. Both red maple and beech
accounted for significantly more of the basal area in this diameter class than oak on the
harvest site and the control site (Figure 3.18).
b) a)
Figure 3.17 Abundance of trees and woody shrubs 1-10 cm DBH on the harvest and control sites in 2006 given by (a) frequency (stems/ha) and (b) basal area (m2/ha). Error bars show bootstrapped 95% confidence intervals.
56
Figure 3.18 Basal area (m2/ha) of trees 1-10 cm DBH in major species groups on the harvest site (red bars) and control site (green bars) in 2006. Error bars show bootstrapped 95% confidence intervals. 3.7 Leaf area index
Before the harvest, there was no significant between-site difference in peak LAI,
which was 5.06 ± 0.57 on the harvest site and 4.96 ± 0.27 on the control site in 1999.
Four and five years after the harvest, peak LAI was significantly lower on the harvest site
at 4.21 ± 0.33 and 4.07 ± 0.38 in 2005 and 2006, respectively, compared to 5.49 ± 0.27
and 5.02 ± 0.32 on the control site. Though peak LAI was lower on the harvest site, there
were no between-site differences in LAI early and late in the growing season (Figure
3.19).
57
b) a)
c)
Figure 3.19 Leaf area index (LAI) measured throughout the growing season before the harvest in (a) 1999, and after the harvest in (b) 2005 and (c)2006 on the harvest site (red lines) and control site (black lines). Error bars show bootstrapped 95% confidence intervals.
58
4. Discussion
4.1 Changes in stand dynamics
Though the overall rate of carbon uptake in live biomass recovered quickly
following the harvest, the increase in growth of smaller trees and early-successional
species suggests that the growth dynamics of the stand were altered. The rapid growth of
species like red maple and black birch typically dominate the early stages of stand
development in New England mixed hardwood forests. Oak may gradually outcompete
these trees and becomes the dominant species (Oliver and Larson, 1996). This is how the
productive red oak stands on the control site developed (Wofsy, 2004), and may be the
future trajectory of the harvest site. However, the high abundance of beech in
comparison to oak in the 1-10 cm DBH size class could indicate that beech will move
into the canopy as early-successional species die out.
The harvest caused a large reduction in total standing biomass and LAI
measurements in 2005 and 2006 showed that the canopy has not fully recovered on the
harvest site. Despite this, carbon uptake in live biomass was not significantly different
between the sites in 2002 through 2006, indicating that the rapid growth of early-
successional species following the harvest gave this pool a high degree of resilience. It
also suggests that the rate of carbon uptake in growth by a young, early-successional
stand is similar to that of a mature mid-successional stand, like the red oaks on the
control site. If growth on the harvest site increases over the next 10 to 15 years, uptake
could exceed the control site, leading to a peak in uptake in the early-successional stage.
If this is the case, it is uncertain what the carbon dynamics of a stand that is between an
early-successional peak and the mid-successional stands in which increasing growth rates
59
have been observed (Pederson, 2005; Urbanski et al., in press). These growth dynamics
also imply that the pattern of productivity during stand development and succession in
these forests may be more complex than the single peak and decline suggested by
Odum’s model of ecosystem development (Odum, 1969).
After the initial recovery from growth suppression due to the harvest, it does not
appear that there was an upward trend in growth on the harvest site between 2002 and
2006. However, it took over 10 years of data collection before a trend was evident in
uptake at the HFEMS (Urbanski et al., in press), so it may be too early to observe
increasing uptake on the harvest site. The dynamics of carbon uptake as the growth of
early-successional species slows and oak or beech begins to dominate uptake on the
harvest site are uncertain, and measurement of tree growth on the harvest site should
continue to observe this potential transition.
4.2 Coarse woody debris pool dynamics
The harvest affected the distribution of the CWD pool in ways that have
consequences for the rate at which the carbon in this pool will be respired to the
atmosphere. Logs left in slash piles during the harvest seem to be the cause of the large
increase in the volume of CWD in the less decayed classes in 2001 (Figure 3.8). It
appears that most of this volume transitioned to decay class 3 between the 2003 and 2006
CWD surveys, indicating that a similar shift to decay classes 4 and 5 may occur in the
next three to six years. Because the more decayed classes respire more quickly, this
could lead to a period of very rapid carbon flux to the atmosphere from the CWD pool,
possibly altering the annual carbon dynamics of the site. Measurement of this pool
60
should continue to monitor these future dynamics. The three year measurement interval
seemed to effectively capture the major changes in CWD following the harvest.
The harvest also significantly reduced the total volume of snags (Figure 3.9).
This was probably due to snags being knocked over by loggers, though some snags may
have been removed as firewood (L. Hutyra, personal communication). This is consistent
with findings that a selective logging operation that removes one-fourth of live trees also
removes or knocks down about 70% of existing snags (Holloway et al., 2007). As
downed logs, this volume of CWD respires more quickly (Erickson et al., 1985, and
Mattson et al., 1987), returning the carbon to the atmosphere over a shorter time interval,
though respiration may be slower than predicted for downed CWD if the logs in slash
piles are not in contact with the ground.
The importance of standing-downed CWD distribution in determining the overall
decay rate on a site is evident in the differences in modeled decay rate between the two
sites. If only downed CWD is considered, the difference in respiration between the two
sites is much smaller than if standing CWD, which is more prevalent on the control site,
is considered (Table 3.2a). In addition to the consequences of standing CWD volume for
respiration, snags are important for other ecosystem functions, especially for providing
suitable wildlife habitat (Conner et al., 1975; DeGraaf and Shigo, 1985; Healy et al.,
1989).
4.3 Uncertainty in the CWD respiration model
The between-site differences in modeled decay class-specific respiration rates
(Table 3.1) indicate that the CWD respiration model was effective in addressing the
61
effect of differences in micrometeorological conditions on CWD pool dynamics, but
there are some weaknesses in its assumptions. The assumption that standing CWD
respires at 40% of the rate of downed CWD decay comes from studies in northwestern
conifer (Erickson et al. 1985) and clear-cut southeastern (Mattson et al., 1987) forests,
and should be verified with empirical measurements from stands more similar to the site
of this study. The assumption that logs are entirely in contact with the ground and snags
and stumps are not in contact with the ground is also problematic. More of a stump or
short snag is in contact with the ground than a tall snag, and parts of some logs are held
off the ground by other pieces of CWD, especially if they are in slash piles, or are not
entirely in contact with the ground because of their shape. For these reasons, there may
be more variation in respiration rate among pieces of CWD due to degree of contact with
the ground than is represented in the standing-downed difference for which the model
accounts.
The rates at which CWD shifted to more decayed classes and from standing to
downed pools were calculated by tracking individual pieces between surveys so that new
inputs would not be included in the calculated probabilities. For a piece of CWD to be
identified in a subsequent survey, an aluminum tag nailed into the wood had to stay
attached for two to three years. It appeared that the tags were more likely to fall off of
more decayed wood, so the shift rate to more decayed classes may be underestimated.
This is less of an issue with the standing to downed shift rate because it was calculated by
tracking snags which are almost all in the less decayed classes and thus rarely lose their
tags. However, the rate of input to the downed pools from this standing to downed shift
accounted only for inputs from falling snags. Downed CWD can also be created by
62
branches falling from live trees, which is difficult to simulate in a model, so inputs to
downed CWD may be underestimated.
The models for on-site temperature made the assumption that the relationship
between on-site temperature and tower measurements of temperature and incoming
radiation in 2004 and 2005, when micrometeorological measurements were available,
was the same as in other years. This is more problematic on the harvest site, where it is
likely that the light environment changed in the years following the harvest.
It appears that the model overestimated respiration on the harvest site between the
harvest and the 2003 CWD survey, as this is the only survey year in which the modeled
pool size is smaller than the measured pool size (Figure 3.12). One possible reason for
this is that the temperature model made using micrometeorological data from 2004 and
2005 did not produce an accurate representation of the on-site temperatures in the years
immediately after the harvest. A more open canopy in these years may have caused drier
conditions and less respiration. Also, most of the CWD inputs from the harvest were left
in slash piles where many pieces of wood were held off the ground by other pieces and
may have decayed more slowly than the model predicts.
Despite these issues, the CWD respiration model was useful in linking small-scale
measurement of CWD respiration rate (Liu et al., 2006) to ecosystem-level carbon
dynamics. It also allowed for an approximation of annual CWD pool dynamics, as field
measurement of this pool was not possible on an annual basis.
63
4.4 Uncertainty in soil carbon and belowground biomass fluxes
The largest sources of uncertainty in the carbon budget are the estimates of soil
carbon uptake and belowground biomass fluxes on the harvest site because they were not
measured directly. In a meta-analysis of seven studies on the effect of harvesting on soil
carbon in hardwood forests, Johnson and Curtis (2001) found that average soil carbon
content decreased slightly after harvest, but this effect was not statistically significant and
varied greatly with location and ecosystem type. A study of soil dynamics following a
harvest at the Hubbard Brook Experimental Forest in New Hampshire found that
harvesting caused significant disturbance and compaction of soil but losses of carbon due
to respiration and leaching following the harvest were balanced by inputs from root and
leaf litter breakdown (Johnson et al., 1991). These findings support our assumption that
the immediate effects of the harvest did not result in a significant flux of carbon into or
out of the belowground system. In the years following the harvest, however, the
differences in microclimate between the sites (Table 2.1) may have caused differences in
soil temperature and moisture. A study conducted at the Harvard Forest found that soil
respiration varied spatially and temporally with differences in temperature and moisture
(Davidson et al., 1998), so the between-site microclimatic differences may have affected
overall soil respiration. Peng and Thomas (2006) found that the effect of selective
harvest in a northern hardwood forest on soil respiration was only partially explained by
differences in soil temperature and moisture and is also due to changes in belowground
biomass. They found that soil respiration increased in the year after selective harvest
then declined to below pre-harvest levels and gradually recovered to pre-harvest levels 5-
64
6 years after logging, resulting in an overall soil CO2 efflux slightly below that on
unharvested sites.
The estimate of belowground biomass using a proportion of aboveground biomass
or allometric equations is also problematic as these estimation techniques were developed
for unharvested sites. Oak, maple, and beech, can grow from stump sprouts and maple
and beech from root suckers (Tirmenstein, 1991a; Tirmenstein, 1991b; Coladonato
1991), so the mortality of a tree may not necessarily result in the morality of its entire
root system. For this reason, allometric equations have been found to systematically
underestimate root biomass in young stands (Park et al., 2007). Logging operations can
also damage the roots of unharvested trees (Vasiliauskas, 2001), leading to unaccounted
for belowground mortality. The best way to improve the certainty of the carbon budgets
would be to do a more complete belowground carbon budget including measurement of
litter inputs and soil respiration or by doing a 14C labeling study, as was done for the
control site (Gaudinski et al., 2000).
4.5 Carbon sequestration in wood products
The estimated contribution of wood products to the carbon dynamics of the
harvest is highly dependent on which details are considered in the calculations. Because
so much firewood was removed from the site, consideration of the fossil fuel offset (5.0
MgC/ha) for this wood has a large effect on the overall carbon balance. If a larger
proportion of the wood from this harvest had been sold as sawtimber, whether or not
emissions from fossil fuels used in harvesting and processing wood products was
considered would have been very important, as this flux was equal to nearly half (46%)
65
of the carbon stored in wood products. Other accounting details, such as fossil fuel
offsets from using wood in place of materials such as steel and concrete, which require
more energy for production (Werner et al., 2005; Gustavsson and Sathre, 2006), were not
considered in this study could further alter the conclusions made about carbon storage in
wood products.
Under the assumptions used in this study, 2.0 MgC/ha were stored in wood
products from removed sawtimber after fossil fuel emissions in harvesting and processing
were accounted for. Though nearly all of this carbon remained in use or in landfills over
the time period considered in this study, wood products are not a permanent carbon sink.
As they are disposed, a significant portion of their carbon will be returned to the
atmosphere as landfill gases over the next 100-200 years (Figure 3.16). If landfill gas
capture and combustion are not used, this is more damaging from a climate change
perspective than simply burning the wood because of the global warming potential of the
methane released.
4.6 Source-sink dynamics of the harvest site
The carbon budgets for the harvest and control sites indicate that in the first five
years, harvesting does not increase carbon uptake. Even after considering wood products
as a sink and applying fossil fuel offsets, uptake on the harvest site will have to be greater
than uptake on the control site by an average of 0.4 MgC ha-1 yr-1 over the next 20 years
for the harvest site to be a carbon sink of a magnitude similar to the control site on a 25-
year time scale. The future carbon dynamics of the site will depend on the balance
between uptake in new growth as the stand transitions from early- to mid-successional
66
and CWD respiration as the inputs from the harvest move into more decayed classes.
While the harvest site was a smaller net carbon sink than the control site cumulatively
since the harvest, the site recovered quickly to a net annual carbon flux that was similar
to the control site just three years after the harvest.
4.7 Management implications
Though the data from this study do not suggest that New England forests should
be selectively logged with the goal of increasing carbon sequestration, they do show that
these forests are resilient and, with proper management, could provide a sustainable
source of wood products for the Northeast without seriously compromising carbon
uptake. Currently, only 2% of the wood and paper products used in Massachusetts are
produced locally, while the majority of Massachusetts forests are not harvested to their
sustainable yield. Increased local production could alleviate logging pressure in other
parts of the country and the world, decrease fossil fuel emissions used in transporting
wood, and provide local economic opportunities, especially in small towns (Berlik et al.,
2002).
Because carbon uptake rates in mid-successional New England hardwood forests
do not appear to decline with stand age (Peterson, 2005; Urbanski et al., in press), the
carbon sequestration potential of managed forests could probably be improved by
increasing the amount of time between harvests. This allows the forest to grow and store
carbon, offsetting short-term losses associated with harvesting. It is also possible that
carbon sequestration in managed forests could be increased through changes to the
species selection of harvests. Red oak and white pine, which are highly productive as
67
large canopy trees, are preferentially harvested because of their economic value
(Kittredge et al., 2003). This approach to harvesting selects for the less-frequently cut
species (Oliver and Larson, 1996), in this case, less productive species such as red maple,
birch, and hemlock. Using wood in place of other construction materials such as steel
and concrete has been suggested as a way that New England forest management could
contribute to emissions reduction (D. Foster, personal communication), though the
potential release of carbon contained in the wood to the atmosphere after disposal and
emissions in processing must be considered. Forest management with this goal must be
done with a well-designed management plan that is careful to avoid further increasing the
preferential harvest of red oak and white pine, as these species are valuable as
construction material.
Increasing time between harvests and cutting less productive species would
decrease short-term profit from forest harvesting and increase the value of future
harvests, making these difficult strategies to encourage because most landowners in
Massachusetts are not in a position to benefit from trading short-term profits for long-
term benefit. Most private forest owners only own their land for about 20 years (Foster et
al., 2005), and development is encroaching on many forested areas (McDonald et al.,
2006), giving landowners the option of selling land to developers in the near future. In
this situation, creating an economic incentive for management plans that increase carbon
sequestration, such as incorporation of forest offsets in a cap-and-trade program, may be
the best way to encourage the participation of land owners.
Sampson (2004) outlined several models for incorporating carbon sequestration
from forestry and agriculture into cap-and-trade programs. Of these, the “business as
68
usual” (BAU) model seems to be the best structure for incorporating forestry practices in
a landscape where the forests will take up carbon in the absence of management. Under
the BAU model, a baseline is established as the carbon uptake that would occur under
normal growth conditions. Offsets or emissions are then credited or debited as the
change from this baseline. Modifying a harvest plan to minimize the reduction of carbon
uptake could then be an economically viable option, even if it decreased the profit from
the harvested wood, because it would reduce the emissions credited to the land owner.
More study of the effects of specific management techniques on carbon uptake in
different types of forests would be needed before a program like this could be
implemented. While this could be a useful tool in influencing land owners, it shouldn’t
be the only oversight on forest harvesting. Carbon sequestration is just one of a group of
goals for forest management that address concerns from the local to the global scale.
69
5. Conclusions
Five years after selective logging, the harvest site is a small cumulative net carbon
sink. If the use of wood removed in the harvest is considered, the system is a carbon sink
in relation to the atmosphere but stored much less carbon over the five year period than
the control site. The future carbon dynamics of the harvest site will depend strongly on
the trajectories of growth and woody debris respiration, and biometric measurement of
these pools should continue to investigate this. The overall certainty of these conclusions
would be improved by a more careful treatment of the belowground carbon budget. The
data from this study do not suggest that more intensive forest management should be
initiated in New England with the goal of increasing carbon sequestration unless further
measurement of this site shows an increase in uptake over the next 5-10 years. However,
carbon uptake rates on the harvest site recovered quickly after the harvest, suggesting that
these forests are resilient and could provide a sustainable source of wood products as part
of a management plan that considers carbon sequestration among a broad spectrum of
goals for forest management in New England.
70
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Appendix A Allometric equations for calculating the weight (wt) of live trees and whole tree snags from DBH or basal diameter (BD). Weight is in pounds and diameter is in inches unless otherwise noted. Species Equation Source American Beech ln(wt) = 1.3303 + 2.2988 ln(DBH) Young et al. 1980 (Fagus grandifolia) Black Birch wt = 1.6542 (DBH)2.6606 Brenneman et al.1978 (Betula lenta) Black Cherry wt = 1.8082 (DBH)2.6174 Brenneman et al.1978 (Prunus serotina) Gray Birch ln(wt) = 1.0931 + 2.3146 ln(DBH) Young et al. 1980 (Betula populifolia) Highbush Blueberry* wt = 95.143 (BD)3.706 Telfer 1969 (Vaccinium corymbosum) Based on wt in g and BD in cm Eastern Hemlock ln(wt) = 0.6803 + 2.3617 ln(DBH) Young et al. 1980 (Tsuga canadensis) Eastern White Pine ln(wt) = 0.4080 + 2.4490 ln(DBH) Young et al. 1980 (Pinus strobus) Hawthorn ln(wt) = -2.48 + 2.4835 ln(DBH) Jenkins et al. 2003 (Crataegus spp.) Based on wt in kg and DBH in cm Northern Red Oak wt = 2.4601 (DBH)2.4572 Brenneman et al.1978 (Quercus rubra) Northern Wild Raisin** wt = 29.615 (BD)3.243 Telfer 1969 (Viburnum cassinoides) Based on wt in g and BD in cm Red Maple ln(wt) = 0.9392 + 2.3804 ln(DBH) Young et al. 1980 (Acer rubrum) Red Pine ln(wt) = 0.7157 + 2.3865 ln(DBH) Young et al. 1980 (Pinus resinosa) Spirea wt = 36.648 (BD)2.579 Telfer 1969 (Spirea spp.) Based on wt in g and BD in cm
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White Ash wt = 2.3626 (DBH)2.4798 Brenneman et al.1978 (Fraxinus americana) White/Paper Birch ln(wt) = 0.4792 + 2.6634 ln(DBH) Young et al. 1980 (Betula papyrifera) White Oak wt = 1.5647 (DBH)2.6887 Brenneman et al.1978 (Quercus alba) White Spruce† ln(wt) = 0.8079 + 2.3316 ln(DBH) Young et al. 1980 (Picea glauca) Witch Hazel [0.001*10(-3.037 + 2.900 log10(BD*10))] – Telfer 1969 (Hamamelis virginianaia) [0.001*10-2.0729 + 2.162 log10(BD*10))] Yellow Birch ln(wt) = 1.1297 + 2.3376 ln(DBH) Young et al. 1980 (Betula alleghaniensis) *Equation calculated for Vaccinium spp. **Equation calculated for Viburnum lantanoides † Equation calculated for Picea spp. Equation for northern red oak also used for black oak (Quercus velutina) and American chestnut (Castanea dentata). Equation for red maple also used for striped maple (Acer pensylvanicum).
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Appendix B WD measurement and calculation information B.1 CWD Classifications (Harmon and Sexton, 1996) Classification Description Log horizontal (< 45° off the ground), ≥ 7.5 cm in diameter at larger
end, ≥ 1 m long Log Snag standing (≥ 45° off the ground) bole, ≥ 7.5 cm in diameter at the
base, ≥ 1 m in height Stump standing (≥ 45° off the ground) bole, ≥ 7.5 cm in diameter at the
base, ≤ 1 m in height Whole Tree Snag standing (≥ 45° off the ground) bole, ≥ 7.5 cm in diameter at the
base, retains the structure of a live tree, twigs and branches still intact
B.2 Volume Equations (Harmon and Sexton, 1996) Logs (Newton’s Formula): V = L(Ab + 4Am +At)/6 Log Snags (Frustrum of a Cone): V = L(Ab + sqrt(AbAt) + At)/3 Stumps (Frustrum of a Cone): V = L(Ab + sqrt(AbAt) + At)/3 Where: V = volume in m3 L = length in m Ab = Cross-sectional area at the larger end/base in m2 Am = Cross-sectional area at the mid-point in m2 At = Cross-sectional area at the smaller end/top in m2 FWD Volume per Area: V = Ac / (8 L) Where: V = volume per area in m3/m2 Ac = Cross-sectional area of FWD on transect segment L = transect segment length in m B.3 Physical Descriptions of WD Decay Classes (Harmon and Sexton, 1996) Decay Class Description I Solid wood, recently fallen, bark and twigs present. II Solid wood, bark and branches still present.
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III Wood not solid, some sloughing of sapwood, but nail must still be pounded into log.
IV Sapwood sloughing, nail may be forcibly pushed into log.
V Heartwood friable, barely holding shape, nail may be easily pushed into log.
B.4 WD Density Values (Liu et al., 2006) Decay Class Bulk Density (g/cm3) I 0.47 II 0.41 III 0.31 IV – V 0.23
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Appendix C Parameters and conversion factors used in wood products accounting. C.1 Conversion factors Input Value Source Gasoline consumption, chainsaw 0.275 l/m3 Raymer (2006) Diesel consumption, harvester 1.1 l/m3 Raymer (2006) Diesel consumption, tractor 2.0 l/m3 Raymer (2006) Diesel consumption, heavy transport 0.45 l/km Raymer (2006) Maximum load, heavy transport 27,500 kg Raymer (2006) Distance from logging site to sawmill 13.3 km Green density, Birch 0.90 g/cm3 Liu (2006) Green density, Oak 0.93 g/cm3 Liu (2006) Green density, Maple 0.88 g/cm3 Liu (2006) Green density, Hemlock 0.70 g/cm3 Liu (2006) Green density, All species 0.90 g/cm3 Liu (2006) Wood density, Red Oak 0.56 g/cm3 Jenkins et al. (2003) Wood density, White Oak 0.60 g/cm3 Jenkins et al. (2003) Wood density, Black Oak 0.56 g/cm3 Jenkins et al. (2003) Wood density, White Ash 0.55 g/cm3 Jenkins et al. (2003) Wood density, Hemlock 0.38 g/cm3 Jenkins et al. (2003) Wood density, White Pine 0.34 g/cm3 Jenkins et al. (2003) Wood density, Black Birch 0.60 g/cm3 Jenkins et al. (2003) Wood density, White Birch 0.48 g/cm3 Jenkins et al. (2003) Wood density, Beech 0.56 g/cm3 Jenkins et al. (2003) Wood density, Red Maple 0.49 g/cm3 Jenkins et al. (2003) Weighted average biomass, firewood 772 kgC/cord Kuhns and Schmidt (2003) Efficiency, oil-fueled central heating 80% DOE (2005) Efficiency, catalytic wood stove 70% DOE (2005) Emissions, fuel oil (No. 2) 21.0 kgC/million BTU Aubé (2001) Emissions, diesel 0.746 kgC/l CORRIM (2001) Emissions, gasoline 0.643 kgC/l CORRIM (2001) Heat content, Beech 32300 BTU/kgC Kuhns and Schmidt (2003) Heat content, Birch 30600 BTU/kgC Kuhns and Schmidt (2003) Heat content, Hemlock 31500 BTU/kgC Kuhns and Schmidt (2003) Heat content, Maple 30500 BTU/kgC Kuhns and Schmidt (2003) Heat content, Red/Black Oak 30700 BTU/kgC Kuhns and Schmidt (2003) Heat content, White Ash 30700 BTU/kgC Kuhns and Schmidt (2003) Heat content, White Oak 30500 BTU/kgC Kuhns and Schmidt (2003) Heat content, White Pine 31100 BTU/kgC Kuhns and Schmidt (2003) Heat content, weighted average 31000 BTU/kgC Carbon content, paper 44% Kubeczko (2001) Energy use, paper production 38,600 BTU/kg paper EPA (2007) Production-related emissions (for 1 kgC in sawtimber) Construction beams 0.255 kgC Werner et al. (2005) Floor joists 0.483 kgC Werner et al. (2005)
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Laminated timber board 0.134 kgC Werner et al. (2005) Rough panels and supports 0.0214 kgC Werner et al. (2005) Furniture 0.603 kgC Werner et al. (2005) Recycling rate, paper products 50.0% EPA (2005) Recycling rate, wood 9.4% EPA (2005) Percent of MSW burned for energy 13.8% EPA (2005) C.2 HARVCARB equations for product retirement from end-use pools (Row and Phelps 1996) Original expected median life in use (H) is adjusted to account for the proportion of each use recycled (R) to give adjusted expected median life in use (L):
L = H / (1 – R) The equation used to find the pattern of product retirement (P) depends on the time in the sink (T). When T exceeds L:
P = 0.5 / [1 + 2 (ln(T) – ln(L))]
When T is between ½ L and L, P is interpolated between 0.5 and:
P = 1 – 0.5 / (1 + 2 ln(L))
When T is less than ½ L, P is interpolated between 1 and P at T = ½ L. C.3 Statistics for the Martone Landfill in Barre, MA, used as inputs for LandGEM v3.02 (DEP, 2006). Open Date 1968 Scheduled Close Date 12/31/2010 Annual Acceptance (Tons)
Year Acceptance Year Acceptance 1990 62400 1998 53686 1991 62400 1999 96575 1992 93600 2000 97471 1993 93600 2001 93578 1994 97499 2002 93390 1995 96393 2003 93522 1996 94141 2004 93534 1997 48904 2005 93557
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